In [5]:
import os, sys, io
import datetime
import joblib
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook, tnrange

from keras.layers import (Input, Dense, LSTM, Dropout, TimeDistributed,
                          AveragePooling1D, Flatten, InputLayer, BatchNormalization)
from keras.layers.core import Reshape
from keras.models import Model, Sequential
from keras.optimizers import Adam
from keras.callbacks import TensorBoard
from keras import initializers
import tensorflow as tf

from sklearn import model_selection

sys.path.append('%s/lib' % (os.path.abspath('..')))
from SyntheticLCGenerator import synthetic_light_curve_generator
In [2]:
# Let Keras know that we are using tensorflow as our backend engine
os.environ["KERAS_BACKEND"] = "tensorflow"

# Main Path
main_path = os.path.abspath('..')

# To make sure that we can reproduce the experiment and get the same results
np.random.seed(10)
In [20]:
def load_synthetic_time_series(data_path=None, n_samples=14000, seq_length=50,
                               time_span=4, n_bands=1, n_signals=1, SNR_min=5,
                               f0_interval='narrow',
                               use_time=True, use_err=True):

    if f0_interval == 'narrow':
        f0_inter = [1/10., 1/1.]
    elif f0_interval == 'wide':
        f0_inter = [1/100., 1/0.01]
    else:
        print('Plese define frequency interval...')
        return

    if os.path.exists(data_path):
        print('Loading from: ', data_path)
        aux = np.load(data_path).item()
        samples = aux['samples']
        labels = np.array(aux['labels'])[:, None]
        periods = aux['periods']
        if n_bands == 1:
            samples = samples.reshape(samples.shape[0], samples.shape[2],
                                      samples.shape[3])
        del aux
        if use_time and not use_err:
            samples = samples[:, :, 0:2]
        if not use_time and not use_err:
            samples = samples[:, :, 1:2]
    return samples, labels, periods


def load_real_time_series(data_path=None, survey='EROS2', n_bands=2,
                          use_time=True, use_err=True):

    if os.path.exists(data_path):
        print('Loading from: ', data_path)
        aux = joblib.load(data_path)
        lcs = aux['lcs']
        meta = aux['meta']
        del aux
        lcs = np.stack([x.values for x in lcs])
        print(lcs.shape)
        if n_bands == 1:
            lcs = lcs[:, :, 0:3]
        if use_time and not use_err:
            lcs = lcs[:, :, 0:2]
        if not use_time and not use_err:
            lcs = lcs[:, :, 1:2]
    return lcs, meta
In [78]:
# You will use the Adam optimizer
def get_optimizer(learning_rate):
    return Adam(lr=learning_rate, beta_1=0.5)


def get_generator(optimizer, G_hidden_units, latent_dim, 
                  out_lc_len, n_feat, dropout=0.1):
    generator = Sequential()

    # LSRM ouputs last state
    # generator.add(LSTM(G_hidden_units, input_shape=(out_lc_len, latent_dim),
    #                    return_sequences=False))
    # generator.add(Dense(out_lc_len*n_feat, activation='tanh'))
    # generator.add(Reshape((out_lc_len, n_feat)))
    
    # using all LSTM outputs (sequence)
    generator.add(LSTM(G_hidden_units, input_shape=(out_lc_len, latent_dim),
                       return_sequences=True))
    generator.add(Dropout(dropout))
    
    # generator.add(LSTM(G_hidden_units,
    #                    return_sequences=True))
    # generator.add(Dropout(dropout))

    generator.add(TimeDistributed(Dense(n_feat, activation='tanh')))
    
    # fully connected layers
    # generatortor.add(Dense())

    generator.compile(loss='mean_squared_error', optimizer=optimizer)
    return generator


def get_discriminator(optimizer, D_hidden_units, lc_len,
                      n_feat, dropout=0.1, D_output=1):
    discriminator = Sequential()

    # LSTM output last state
    # discriminator.add(LSTM(D_hidden_units, input_shape=(lc_len, n_feat),
    #                        return_sequences=False, 
    #                        kernel_initializer=initializers.RandomNormal(stddev=0.02)))
    # discriminator.add(Dense(5, activation='tanh'))
    # discriminator.add(Dropout(dropout))
    # discriminator.add(Dense(D_output, activation='sigmoid'))
    
    
    # using all LSTM states, full sequence
    # discriminator.add(BatchNormalization(axis=-1, input_shape=(lc_len, n_feat)))
    discriminator.add(LSTM(D_hidden_units, input_shape=(lc_len, n_feat),
                           return_sequences=True))
    discriminator.add(Dropout(dropout))
    
    # discriminator.add(LSTM(D_hidden_units,
    #                        return_sequences=True,
    #                        kernel_initializer=initializers.RandomNormal(stddev=0.02)))
    # discriminator.add(Dropout(dropout))
    
    discriminator.add(TimeDistributed(Dense(D_output, activation='softmax')))
    discriminator.add(AveragePooling1D(pool_size=lc_len, data_format='channels_last'))
    discriminator.add(Flatten())

    discriminator.compile(loss='binary_crossentropy',
                          optimizer=optimizer, metrics=['acc'])
    return discriminator
In [68]:
def get_gan_network(discriminator, latent_dim, out_lc_len, generator, optimizer):

    # We initially set trainable to False since we only want
    # to train either the generator or discriminator at a time
    discriminator.trainable = False

    # gan input (noise) will be 100-dimensional vectors
    gan_input = Input(shape=(out_lc_len, latent_dim))

    # the output of the generator (an image)
    x = generator(gan_input)

    # get the output of the discriminator
    # (probability if the image is real or not)
    gan_output = discriminator(x)
    gan = Model(inputs=gan_input, outputs=gan_output)
    gan.compile(loss='binary_crossentropy', optimizer=optimizer)

    return gan

Extra functions

In [9]:
# Create a wall of generated time series

def plot_generated_time_series(epoch, generated_lc, test_lc=None, examples=8,
                               dim=(2, 4), figsize=(16, 4)):

    fig, ax = plt.subplots(nrows=dim[0], ncols=dim[1], figsize=figsize)
    for i in range(dim[0]):
        for j in range(dim[1]):
            if use_time and use_err:
                if j == 0 and test_lc is not None:
                    ax[i,j].errorbar(test_lc[j+i*i, :, 0], test_lc[j+i*i, :, 1],
                                     yerr=test_lc[j+i*i, :, 2], fmt='k.')
                else:
                    ax[i,j].errorbar(generated_lc[(i-1)+i*j, :, 0], generated_lc[(i-1)+i*j, :, 1],
                                     yerr=generated_lc[(i-1)+i*j, :, 2], fmt='b.')
            elif use_time and not use_err:
                if j == 0 and test_lc is not None:
                    ax[i,j].plot(test_lc[j+i*i, :, 0], test_lc[j+i*i, :, 1], 'k.')
                else:
                    ax[i,j].plot(generated_lc[(i-1)+i*j, :, 0], generated_lc[(i-1)+i*j, :, 1], 'b.')
            elif not use_time and not use_err:
                if j == 0 and test_lc is not None:
                    ax[i,j].plot(test_lc[j+i*i, :], 'k.')
                else:
                    ax[i,j].plot(generated_lc[(i-1)+i*j, :], 'b.')
    plt.tight_layout()
    # plt.savefig('gan_generated_image_epoch_%d.png' % epoch)
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    plt.show()
    return buf
In [10]:
def write_log_scalar(callback, names, logs, step):
    for name, value in zip(names, logs):
        summary = tf.Summary()
        summary_value = summary.value.add()
        summary_value.simple_value = value
        summary_value.tag = name
        callback.writer.add_summary(summary, step)
        callback.writer.flush()
        
def write_log_plot(callback, buf, step):
    image = tf.image.decode_png(buf.getvalue(), channels=4)
    image = tf.expand_dims(image, 0)
    summary = tf.summary.image("Generated_Image", image,
                               max_outputs=1)
    callback.writer.add_summary(tf.keras.backend.eval(summary), step)
    callback.writer.flush()
In [11]:
def normalize(data, norm_time=False, scale_to=[0, 1], n_feat=2):
    normed = np.zeros_like(data)
    for i, lc in enumerate(data):
        normed[i, :, n_feat-1] = (lc[:, n_feat-1] - np.min(lc[:, n_feat-1])) / \
            (np.max(lc[:, n_feat-1]) - np.min(lc[:, n_feat-1]))
        if scale_to != [0, 1]:
            normed[i, :, n_feat-1] = (normed[i, :, n_feat-1] * (scale_to[1] - scale_to[0])) + scale_to[0]
        if norm_time:
            normed[i, :, n_feat-2] = (lc[:, n_feat-2] - np.min(lc[:, n_feat-2])) / \
                (np.max(lc[:, n_feat-2]) - np.min(lc[:, n_feat-2]))
            if scale_to != [0, 1]:
                normed[i, :, n_feat-2] = (normed[i, :, n_feat-2] * (scale_to[1] - scale_to[0])) + scale_to[0]
        else:
            normed[i, :, n_feat-2] = lc[:, n_feat-2]
    return normed


def standarize(data, stand_time=False):
    standar = np.zeros_like(data)
    for i, lc in enumerate(data):
        standar[i, :, 1] = (lc[:, 1] - np.mean(lc[:, 1])) / np.std(lc[:, 1])
        if stand_time:
            standar[i, :, 0] = (lc[:, 0] - np.mean(lc[:, 0])) / np.std(lc[:, 0])
        else:
            standar[i, :, 0] = lc[:, 0]
    return standar


def get_noise(n_samples, n_timesteps=50, latent_dim=5, use_time=True):
    latent = np.random.normal(size=[n_samples, n_timesteps, latent_dim])
    if use_time:
        latent[:, :, 0] = sample_time(n=n_timesteps)
    return latent


def sample_time(n=50, t_range=[0, 4], irregular=True):
    time = np.linspace(t_range[0], t_range[1], num=n*10)
    time = np.random.choice(time, size=n, replace=False)
    return np.sort(time)

Tsting area

test = standarize(samples[:2], stand_time=True) plt.plot(samples[0,:,0], samples[0,:,1], '.k') plt.plot(test[0,:,0], test[0,:,1], '.r') plt.show()test_d = Sequential() test_d.add(LSTM(10, input_shape=(50,3), return_sequences=True)) test_d.add(Dense(1, activation='sigmoid')) test_d.add(AveragePooling1D(50)) test_d.add(Reshape((1, ))) test_d.compile(loss='mean_squared_error', optimizer='adam') print(test_d.summary()) print('LSTM 1 output shape: ',test_d.predict(samples[0:10]).shape)test_g = Sequential() test_g.add(LSTM(5, input_shape=(100,3), return_sequences=True)) test_g.add(TimeDistributed(Dense(3))) test_g.compile(loss='mean_squared_error', optimizer='adam') print(test_g.summary()) print('LSTM 1 output shape: ',test_g.predict(test_noise).shape)print('Original lc: ', samples[0].mean(), samples[0].std()) for k in range(1): plt.plot(samples[k], '.') plt.show() # define model inputs1 = Input(shape=(50, 1)) lstm1 = BatchNormalization(axis=-1)(inputs1) model = Model(inputs=inputs1, outputs=lstm1) norm = test_d.predict(samples[0]) print('Batch Norm output shape: ', norm.mean(), norm.std()) plt.plot(norm, '.') plt.show()

3. Settings

In [84]:
# data
n_samples = 28000
n_obs = 50
n_bands = 1
use_time = True
use_err = False
n_feat = 3
if (use_time and not use_err) or (not use_time and use_err):
    n_feat = 2
elif not use_time and not use_err:
    n_feat = 1
data_name = 'SynSine_time%s_err%s' % (str(use_time)[0], str(use_err)[0])
# data_name = 'EROS2_trim215_augmented'

# GAN architecture
model_name = 'LSTM_GAN'
learning_rate = 0.5
latent_dim = 5
D_hidden_units = 4
G_hidden_units = 4
D_output = 1
G_dropout = 0.2
D_dropout = 0.1
batch_size = 28
n_epochs = 200
N_gen_feat = n_feat
save_interval = 5
viz_interval = 10
batch_count = round(7000*.6 / batch_size)
n_examples = 8
gen_lc_len = n_obs

2. Retrieve data

data_path = '%s/data/real/EROS2_meta+lcs_sample_lc_trim_augmented.pkl' % (main_path) samples, meta = load_real_time_series(data_path, use_time=use_time, use_err=use_err) samples = normalize(samples[:7000], norm_time=False, scale_to=[-1, 1], n_feat=n_feat) meta = meta[:7000] print('Samples shape: ', samples.shape) [x_train, x_test, y_train, y_test] = model_selection.train_test_split(samples, meta, train_size=0.6)
In [80]:
data_path = ('%s/data/synthetic/sine_nsamples%i_seqlength%i_nbands%i_nsig%i_timespan%i_SNR%i_f0%s.npy'
             % (main_path, n_samples, n_obs, n_bands, 1, 4, 40, 'narrow'))
samples, labels, periods = load_synthetic_time_series(data_path, use_time=use_time, use_err=use_err)

samples = normalize(samples[:7000], norm_time=False, scale_to=[-1, 1], n_feat=n_feat)
labels = labels[:7000]
periods = periods[:7000]

print('Samples shape: ', samples.shape)

[x_train, x_test, 
 y_train, y_test] = model_selection.train_test_split(samples, labels, 
                                                     train_size=0.6)
Loading from:  /Users/jorgetil/Astro/TL-GANs/data/synthetic/sine_nsamples28000_seqlength50_nbands1_nsig1_timespan4_SNR40_f0narrow.npy
Samples shape:  (7000, 50, 2)
In [86]:
for n_units in [2,4,8,16,32,64]:
    # Build our GAN netowrk
    print('########## N units %i ##########' % (n_units))
    adam = get_optimizer(learning_rate)
    generator = get_generator(adam, n_units, latent_dim,
                              n_obs, n_feat, dropout=G_dropout)
    discriminator = get_discriminator(adam, n_units, n_obs,
                                      n_feat, dropout=D_dropout, 
                                      D_output=D_output)
    print('generator')
    print(generator.summary())
    print('discriminator')
    print(discriminator.summary())
    gan = get_gan_network(discriminator, latent_dim, gen_lc_len, generator, adam)
    print('GAN')
    print(gan.summary())

    test_noise = get_noise(n_examples, latent_dim=latent_dim)

    # logger = TensorBoard(log_dir='%s/logs/%s_%s/run_%s/' % 
    #                      (main_path, model_name, data_name,
    #                       datetime.datetime.now().strftime("%y%m%d_%I%M")),
    #                      histogram_freq=0, write_graph=False, write_images=True)
    # logger.set_model(gan)
    loss_G, loss_D = [], []

    for e in range(1, n_epochs+1):
        print('-'*15, 'Epoch %d' % e, '-'*15)
        for n_batch in tnrange(batch_count, desc='# batch'):

            # Get a random set of input noise and real LC's
            real_lc = x_train[n_batch*batch_size: (n_batch+1)*batch_size]
            real_lc_y = np.random.uniform(.8, 1., size=batch_size)
            # real_lc_y = np.ones(batch_size)

            # Generate fake LC's
            noise = get_noise(batch_size, latent_dim=latent_dim)
            gen_lc = generator.predict(noise)
            gen_lc_y = np.random.uniform(0., .2, size=batch_size)
            # gen_lc_y = np.zeros(batch_size)

            # Train discriminator on real
            discriminator.trainable = True
            d_loss_real, d_acc_real = discriminator.train_on_batch(real_lc, real_lc_y)

            # Train discriminator on generated
            discriminator.trainable = True
            d_loss_gen, d_acc_gen = discriminator.train_on_batch(gen_lc, gen_lc_y)

            d_loss = d_loss_real + d_loss_gen
            d_acc = (d_acc_real + d_acc_gen)/2

            # Train generator
            noise = get_noise(batch_size, latent_dim=latent_dim)
            y_gen = np.random.uniform(0.8, 1., size=batch_size)
            # y_gen = np.ones(batch_size)
            discriminator.trainable = False
            g_loss = gan.train_on_batch(noise, y_gen)
            
            loss_G.append([(e-1) * batch_count + n_batch ,g_loss])
            loss_D.append([(e-1) * batch_count + n_batch ,d_loss])

            # if n_batch % 10 == 0:
            #     write_log_scalar(logger, ['G_loss', 'D_loss', 'D_acc'],
            #                      [g_loss, d_loss, d_acc],
            #                      (e-1) * batch_count + n_batch)

        print("%d [D loss: %f, acc: %.0f%%] [G loss: %f]" %
              (e, d_loss, d_acc*100, g_loss))
        if d_loss == 0:
            print('Failiure mode...')
            break

        if e == 1 or e % viz_interval == 0:
            lc_test = generator.predict(get_noise(n_examples, latent_dim=latent_dim))
            img_buf = plot_generated_time_series(e, lc_test,
                                                 test_lc=x_test[np.random.randint(0,len(x_test),8)])

            # generator.save('%s/experiments/params/%s_%s_G_%i.hdf5' %
            #               (main_path, model_name, data_name, e))
            # discriminator.save('%s/experiments/params/%s_%s_D_%i.hdf5' %
            #                   (main_path, model_name, data_name, e))

            # write_log_plot(logger, img_buf, (e-1) * batch_count + n_batch)
    
    loss_G = np.array(loss_G)
    loss_D = np.array(loss_D)
    plt.plot(loss_G[:,0], loss_G[:,1], '.-b', label='G')
    plt.plot(loss_D[:,0], loss_D[:,1], '.-r', label='D')
    plt.xlabel('batch iter')
    plt.ylabel('Loss')
    plt.legend(loc='best')
    plt.show()
########## N units 2 ##########
generator
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_56 (LSTM)               (None, 50, 2)             64        
_________________________________________________________________
dropout_53 (Dropout)         (None, 50, 2)             0         
_________________________________________________________________
time_distributed_30 (TimeDis (None, 50, 2)             6         
=================================================================
Total params: 70
Trainable params: 70
Non-trainable params: 0
_________________________________________________________________
None
discriminator
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_57 (LSTM)               (None, 50, 2)             40        
_________________________________________________________________
dropout_54 (Dropout)         (None, 50, 2)             0         
_________________________________________________________________
time_distributed_31 (TimeDis (None, 50, 1)             3         
_________________________________________________________________
average_pooling1d_17 (Averag (None, 1, 1)              0         
_________________________________________________________________
flatten_17 (Flatten)         (None, 1)                 0         
=================================================================
Total params: 43
Trainable params: 43
Non-trainable params: 0
_________________________________________________________________
None
GAN
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_17 (InputLayer)        (None, 50, 5)             0         
_________________________________________________________________
sequential_33 (Sequential)   (None, 50, 2)             70        
_________________________________________________________________
sequential_34 (Sequential)   (None, 1)                 43        
=================================================================
Total params: 113
Trainable params: 70
Non-trainable params: 43
_________________________________________________________________
None
--------------- Epoch 1 ---------------
1 [D loss: 16.090469, acc: 0%] [G loss: 1.334681]
--------------- Epoch 2 ---------------
2 [D loss: 16.322367, acc: 0%] [G loss: 1.515718]
--------------- Epoch 3 ---------------
3 [D loss: 16.110876, acc: 0%] [G loss: 1.959358]
--------------- Epoch 4 ---------------
4 [D loss: 16.153021, acc: 0%] [G loss: 1.600304]
--------------- Epoch 5 ---------------
5 [D loss: 15.888271, acc: 0%] [G loss: 1.261447]
--------------- Epoch 6 ---------------
6 [D loss: 16.140659, acc: 0%] [G loss: 1.879982]
--------------- Epoch 7 ---------------
7 [D loss: 15.997652, acc: 0%] [G loss: 1.715241]
--------------- Epoch 8 ---------------
8 [D loss: 16.051643, acc: 0%] [G loss: 1.372260]
--------------- Epoch 9 ---------------
9 [D loss: 16.311188, acc: 0%] [G loss: 1.644311]
--------------- Epoch 10 ---------------
10 [D loss: 15.878527, acc: 0%] [G loss: 1.535769]
--------------- Epoch 11 ---------------
11 [D loss: 15.893427, acc: 0%] [G loss: 1.549921]
--------------- Epoch 12 ---------------
12 [D loss: 15.816778, acc: 0%] [G loss: 1.628249]
--------------- Epoch 13 ---------------
13 [D loss: 16.080563, acc: 0%] [G loss: 1.845839]
--------------- Epoch 14 ---------------
14 [D loss: 16.572254, acc: 0%] [G loss: 1.609182]
--------------- Epoch 15 ---------------
15 [D loss: 16.180027, acc: 0%] [G loss: 1.717197]
--------------- Epoch 16 ---------------
16 [D loss: 15.742356, acc: 0%] [G loss: 1.734036]
--------------- Epoch 17 ---------------
17 [D loss: 16.362185, acc: 0%] [G loss: 1.248849]
--------------- Epoch 18 ---------------
18 [D loss: 16.154510, acc: 0%] [G loss: 1.449968]
--------------- Epoch 19 ---------------
19 [D loss: 15.794659, acc: 0%] [G loss: 1.375706]
--------------- Epoch 20 ---------------
20 [D loss: 15.847167, acc: 0%] [G loss: 1.971324]
--------------- Epoch 21 ---------------
21 [D loss: 15.831545, acc: 0%] [G loss: 1.964868]
--------------- Epoch 22 ---------------
22 [D loss: 16.031120, acc: 0%] [G loss: 1.480292]
--------------- Epoch 23 ---------------
23 [D loss: 15.980981, acc: 0%] [G loss: 1.632683]
--------------- Epoch 24 ---------------
24 [D loss: 15.874225, acc: 0%] [G loss: 1.711079]
--------------- Epoch 25 ---------------
25 [D loss: 16.077185, acc: 0%] [G loss: 1.962956]
--------------- Epoch 26 ---------------
26 [D loss: 15.987985, acc: 0%] [G loss: 1.299706]
--------------- Epoch 27 ---------------
27 [D loss: 15.410089, acc: 0%] [G loss: 1.813402]
--------------- Epoch 28 ---------------
28 [D loss: 15.869802, acc: 0%] [G loss: 1.736107]
--------------- Epoch 29 ---------------
29 [D loss: 15.924341, acc: 0%] [G loss: 1.684877]
--------------- Epoch 30 ---------------
30 [D loss: 15.875277, acc: 0%] [G loss: 1.409630]
--------------- Epoch 31 ---------------
31 [D loss: 15.626996, acc: 0%] [G loss: 1.715541]
--------------- Epoch 32 ---------------
32 [D loss: 16.268150, acc: 0%] [G loss: 1.814315]
--------------- Epoch 33 ---------------
33 [D loss: 15.539872, acc: 0%] [G loss: 1.739169]
--------------- Epoch 34 ---------------
34 [D loss: 16.009247, acc: 0%] [G loss: 1.563704]
--------------- Epoch 35 ---------------
35 [D loss: 15.657756, acc: 0%] [G loss: 1.322414]
--------------- Epoch 36 ---------------
36 [D loss: 15.993721, acc: 0%] [G loss: 1.464018]
--------------- Epoch 37 ---------------
37 [D loss: 16.344233, acc: 0%] [G loss: 1.632681]
--------------- Epoch 38 ---------------
38 [D loss: 16.165003, acc: 0%] [G loss: 1.487320]
--------------- Epoch 39 ---------------
39 [D loss: 15.865001, acc: 0%] [G loss: 1.680658]
--------------- Epoch 40 ---------------
40 [D loss: 15.881458, acc: 0%] [G loss: 1.535838]
--------------- Epoch 41 ---------------
41 [D loss: 16.194637, acc: 0%] [G loss: 1.733013]
--------------- Epoch 42 ---------------
42 [D loss: 16.128191, acc: 0%] [G loss: 1.235572]
--------------- Epoch 43 ---------------
43 [D loss: 16.323584, acc: 0%] [G loss: 1.429929]
--------------- Epoch 44 ---------------
44 [D loss: 15.821351, acc: 0%] [G loss: 1.720514]
--------------- Epoch 45 ---------------
45 [D loss: 16.005831, acc: 0%] [G loss: 1.739212]
--------------- Epoch 46 ---------------
46 [D loss: 15.613419, acc: 0%] [G loss: 1.719136]
--------------- Epoch 47 ---------------
47 [D loss: 15.970171, acc: 0%] [G loss: 1.531049]
--------------- Epoch 48 ---------------
48 [D loss: 15.800820, acc: 0%] [G loss: 1.444967]
--------------- Epoch 49 ---------------
49 [D loss: 15.711746, acc: 0%] [G loss: 1.859882]
--------------- Epoch 50 ---------------
50 [D loss: 15.967937, acc: 0%] [G loss: 1.726363]
--------------- Epoch 51 ---------------
51 [D loss: 16.053509, acc: 0%] [G loss: 1.197875]
--------------- Epoch 52 ---------------
52 [D loss: 15.995323, acc: 0%] [G loss: 1.549875]
--------------- Epoch 53 ---------------
53 [D loss: 15.585988, acc: 0%] [G loss: 1.708431]
--------------- Epoch 54 ---------------
54 [D loss: 15.784609, acc: 0%] [G loss: 1.511411]
--------------- Epoch 55 ---------------
55 [D loss: 15.692596, acc: 0%] [G loss: 1.521782]
--------------- Epoch 56 ---------------
56 [D loss: 16.008160, acc: 0%] [G loss: 1.577313]
--------------- Epoch 57 ---------------
57 [D loss: 15.680762, acc: 0%] [G loss: 1.497625]
--------------- Epoch 58 ---------------
58 [D loss: 16.228004, acc: 0%] [G loss: 1.511232]
--------------- Epoch 59 ---------------
59 [D loss: 16.134501, acc: 0%] [G loss: 1.438702]
--------------- Epoch 60 ---------------
60 [D loss: 16.042364, acc: 0%] [G loss: 1.857622]
--------------- Epoch 61 ---------------
61 [D loss: 15.973356, acc: 0%] [G loss: 1.550626]
--------------- Epoch 62 ---------------
62 [D loss: 15.911154, acc: 0%] [G loss: 1.430784]
--------------- Epoch 63 ---------------
63 [D loss: 15.844288, acc: 0%] [G loss: 1.445024]
--------------- Epoch 64 ---------------
64 [D loss: 16.071001, acc: 0%] [G loss: 1.531125]
--------------- Epoch 65 ---------------
65 [D loss: 16.005135, acc: 0%] [G loss: 1.477401]
--------------- Epoch 66 ---------------
66 [D loss: 16.040169, acc: 0%] [G loss: 1.314774]
--------------- Epoch 67 ---------------
67 [D loss: 15.760334, acc: 0%] [G loss: 1.731992]
--------------- Epoch 68 ---------------
68 [D loss: 15.852043, acc: 0%] [G loss: 1.689998]
--------------- Epoch 69 ---------------
69 [D loss: 15.827004, acc: 0%] [G loss: 1.395313]
--------------- Epoch 70 ---------------
70 [D loss: 15.761239, acc: 0%] [G loss: 1.744371]
--------------- Epoch 71 ---------------
71 [D loss: 15.522649, acc: 0%] [G loss: 1.720522]
--------------- Epoch 72 ---------------
72 [D loss: 15.844193, acc: 0%] [G loss: 1.909280]
--------------- Epoch 73 ---------------
73 [D loss: 15.807892, acc: 0%] [G loss: 1.760467]
--------------- Epoch 74 ---------------
74 [D loss: 16.074190, acc: 0%] [G loss: 1.854620]
--------------- Epoch 75 ---------------
75 [D loss: 16.130793, acc: 0%] [G loss: 1.933882]
--------------- Epoch 76 ---------------
76 [D loss: 16.012655, acc: 0%] [G loss: 1.699295]
--------------- Epoch 77 ---------------
77 [D loss: 15.736042, acc: 0%] [G loss: 1.641678]
--------------- Epoch 78 ---------------
78 [D loss: 16.207117, acc: 0%] [G loss: 1.455015]
--------------- Epoch 79 ---------------
79 [D loss: 15.477221, acc: 0%] [G loss: 1.288132]
--------------- Epoch 80 ---------------
80 [D loss: 15.974344, acc: 0%] [G loss: 1.791574]
--------------- Epoch 81 ---------------
81 [D loss: 15.628366, acc: 0%] [G loss: 1.928919]
--------------- Epoch 82 ---------------
82 [D loss: 15.823129, acc: 0%] [G loss: 1.841071]
--------------- Epoch 83 ---------------
83 [D loss: 15.933781, acc: 0%] [G loss: 1.739003]
--------------- Epoch 84 ---------------
84 [D loss: 16.164131, acc: 0%] [G loss: 1.437387]
--------------- Epoch 85 ---------------
85 [D loss: 16.240690, acc: 0%] [G loss: 1.671986]
--------------- Epoch 86 ---------------
86 [D loss: 15.756493, acc: 0%] [G loss: 1.174878]
--------------- Epoch 87 ---------------
87 [D loss: 15.888556, acc: 0%] [G loss: 1.590592]
--------------- Epoch 88 ---------------
88 [D loss: 15.701475, acc: 0%] [G loss: 1.678882]
--------------- Epoch 89 ---------------
89 [D loss: 16.004004, acc: 0%] [G loss: 1.436685]
--------------- Epoch 90 ---------------
90 [D loss: 16.026707, acc: 0%] [G loss: 1.799755]
--------------- Epoch 91 ---------------
91 [D loss: 16.304672, acc: 0%] [G loss: 1.477628]
--------------- Epoch 92 ---------------
92 [D loss: 15.948680, acc: 0%] [G loss: 1.798449]
--------------- Epoch 93 ---------------
93 [D loss: 16.225899, acc: 0%] [G loss: 1.793364]
--------------- Epoch 94 ---------------
94 [D loss: 15.973787, acc: 0%] [G loss: 1.543505]
--------------- Epoch 95 ---------------
95 [D loss: 15.740634, acc: 0%] [G loss: 1.455097]
--------------- Epoch 96 ---------------
96 [D loss: 16.001369, acc: 0%] [G loss: 1.220690]
--------------- Epoch 97 ---------------
97 [D loss: 16.322483, acc: 0%] [G loss: 1.719164]
--------------- Epoch 98 ---------------
98 [D loss: 16.042782, acc: 0%] [G loss: 1.917426]
--------------- Epoch 99 ---------------
99 [D loss: 15.860650, acc: 0%] [G loss: 1.592637]
--------------- Epoch 100 ---------------
100 [D loss: 15.998013, acc: 0%] [G loss: 1.638976]
--------------- Epoch 101 ---------------
101 [D loss: 16.118111, acc: 0%] [G loss: 1.654528]
--------------- Epoch 102 ---------------
102 [D loss: 15.805776, acc: 0%] [G loss: 1.291530]
--------------- Epoch 103 ---------------
103 [D loss: 15.734573, acc: 0%] [G loss: 1.737813]
--------------- Epoch 104 ---------------
104 [D loss: 15.846859, acc: 0%] [G loss: 1.563967]
--------------- Epoch 105 ---------------
105 [D loss: 16.038132, acc: 0%] [G loss: 1.445092]
--------------- Epoch 106 ---------------
106 [D loss: 16.359180, acc: 0%] [G loss: 1.761111]
--------------- Epoch 107 ---------------
107 [D loss: 15.847732, acc: 0%] [G loss: 1.774363]
--------------- Epoch 108 ---------------
108 [D loss: 16.295961, acc: 0%] [G loss: 1.531832]
--------------- Epoch 109 ---------------
109 [D loss: 15.988876, acc: 0%] [G loss: 1.422396]
--------------- Epoch 110 ---------------
110 [D loss: 15.733454, acc: 0%] [G loss: 1.446804]
--------------- Epoch 111 ---------------
111 [D loss: 16.135559, acc: 0%] [G loss: 1.509294]
--------------- Epoch 112 ---------------
112 [D loss: 16.215752, acc: 0%] [G loss: 1.383567]
--------------- Epoch 113 ---------------
113 [D loss: 15.756694, acc: 0%] [G loss: 1.477310]
--------------- Epoch 114 ---------------
114 [D loss: 16.546799, acc: 0%] [G loss: 1.503590]
--------------- Epoch 115 ---------------
115 [D loss: 16.234333, acc: 0%] [G loss: 1.834148]
--------------- Epoch 116 ---------------
116 [D loss: 15.942552, acc: 0%] [G loss: 1.466355]
--------------- Epoch 117 ---------------
117 [D loss: 15.756870, acc: 0%] [G loss: 1.835293]
--------------- Epoch 118 ---------------
118 [D loss: 15.933353, acc: 0%] [G loss: 1.611349]
--------------- Epoch 119 ---------------
119 [D loss: 16.183468, acc: 0%] [G loss: 1.897902]
--------------- Epoch 120 ---------------
120 [D loss: 16.299046, acc: 0%] [G loss: 1.344442]
--------------- Epoch 121 ---------------
121 [D loss: 15.555529, acc: 0%] [G loss: 1.348526]
--------------- Epoch 122 ---------------
122 [D loss: 15.480910, acc: 0%] [G loss: 1.505197]
--------------- Epoch 123 ---------------
123 [D loss: 15.869847, acc: 0%] [G loss: 1.544816]
--------------- Epoch 124 ---------------
124 [D loss: 15.967034, acc: 0%] [G loss: 1.439583]
--------------- Epoch 125 ---------------
125 [D loss: 16.000292, acc: 0%] [G loss: 1.607303]
--------------- Epoch 126 ---------------
126 [D loss: 15.700588, acc: 0%] [G loss: 1.609682]
--------------- Epoch 127 ---------------
127 [D loss: 16.073584, acc: 0%] [G loss: 1.424999]
--------------- Epoch 128 ---------------
128 [D loss: 16.125965, acc: 0%] [G loss: 1.642853]
--------------- Epoch 129 ---------------
129 [D loss: 15.787867, acc: 0%] [G loss: 1.626886]
--------------- Epoch 130 ---------------
130 [D loss: 15.846158, acc: 0%] [G loss: 1.771078]
--------------- Epoch 131 ---------------
131 [D loss: 15.847765, acc: 0%] [G loss: 1.419220]
--------------- Epoch 132 ---------------
132 [D loss: 15.920157, acc: 0%] [G loss: 1.648502]
--------------- Epoch 133 ---------------
133 [D loss: 15.754363, acc: 0%] [G loss: 1.551232]
--------------- Epoch 134 ---------------
134 [D loss: 16.479700, acc: 0%] [G loss: 1.521842]
--------------- Epoch 135 ---------------
135 [D loss: 15.869585, acc: 0%] [G loss: 1.698590]
--------------- Epoch 136 ---------------
136 [D loss: 15.979133, acc: 0%] [G loss: 1.644996]
--------------- Epoch 137 ---------------
137 [D loss: 16.159163, acc: 0%] [G loss: 1.719817]
--------------- Epoch 138 ---------------
138 [D loss: 15.634013, acc: 0%] [G loss: 1.603046]
--------------- Epoch 139 ---------------
139 [D loss: 15.891032, acc: 0%] [G loss: 1.371556]
--------------- Epoch 140 ---------------
140 [D loss: 16.023933, acc: 0%] [G loss: 1.480251]
--------------- Epoch 141 ---------------
141 [D loss: 15.945363, acc: 0%] [G loss: 1.666912]
--------------- Epoch 142 ---------------
142 [D loss: 16.239893, acc: 0%] [G loss: 1.706493]
--------------- Epoch 143 ---------------
143 [D loss: 16.389677, acc: 0%] [G loss: 1.460232]
--------------- Epoch 144 ---------------
144 [D loss: 15.115733, acc: 0%] [G loss: 1.672036]
--------------- Epoch 145 ---------------
145 [D loss: 15.687506, acc: 0%] [G loss: 1.724014]
--------------- Epoch 146 ---------------
146 [D loss: 16.462563, acc: 0%] [G loss: 1.689087]
--------------- Epoch 147 ---------------
147 [D loss: 16.218042, acc: 0%] [G loss: 1.348304]
--------------- Epoch 148 ---------------
148 [D loss: 16.054974, acc: 0%] [G loss: 1.384198]
--------------- Epoch 149 ---------------
149 [D loss: 15.629042, acc: 0%] [G loss: 1.925551]
--------------- Epoch 150 ---------------
150 [D loss: 15.828870, acc: 0%] [G loss: 1.695027]
--------------- Epoch 151 ---------------
151 [D loss: 16.148933, acc: 0%] [G loss: 1.570188]
--------------- Epoch 152 ---------------
152 [D loss: 15.510374, acc: 0%] [G loss: 1.486983]
--------------- Epoch 153 ---------------
153 [D loss: 16.057400, acc: 0%] [G loss: 1.491346]
--------------- Epoch 154 ---------------
154 [D loss: 16.108675, acc: 0%] [G loss: 1.457571]
--------------- Epoch 155 ---------------
155 [D loss: 15.883446, acc: 0%] [G loss: 1.922918]
--------------- Epoch 156 ---------------
156 [D loss: 15.809853, acc: 0%] [G loss: 1.677683]
--------------- Epoch 157 ---------------
157 [D loss: 16.056704, acc: 0%] [G loss: 1.628642]
--------------- Epoch 158 ---------------
158 [D loss: 16.143688, acc: 0%] [G loss: 1.474989]
--------------- Epoch 159 ---------------
159 [D loss: 15.472681, acc: 0%] [G loss: 1.752803]
--------------- Epoch 160 ---------------
160 [D loss: 15.658184, acc: 0%] [G loss: 1.281774]
--------------- Epoch 161 ---------------
161 [D loss: 15.719446, acc: 0%] [G loss: 1.764688]
--------------- Epoch 162 ---------------
162 [D loss: 16.268837, acc: 0%] [G loss: 1.815275]
--------------- Epoch 163 ---------------
163 [D loss: 15.794940, acc: 0%] [G loss: 1.410602]
--------------- Epoch 164 ---------------
164 [D loss: 15.900259, acc: 0%] [G loss: 1.577147]
--------------- Epoch 165 ---------------
165 [D loss: 15.642395, acc: 0%] [G loss: 1.486016]
--------------- Epoch 166 ---------------
166 [D loss: 16.127665, acc: 0%] [G loss: 1.659095]
--------------- Epoch 167 ---------------
167 [D loss: 15.975731, acc: 0%] [G loss: 1.245090]
--------------- Epoch 168 ---------------
168 [D loss: 15.707597, acc: 0%] [G loss: 1.597879]
--------------- Epoch 169 ---------------
169 [D loss: 15.684899, acc: 0%] [G loss: 1.701990]
--------------- Epoch 170 ---------------
170 [D loss: 16.115356, acc: 0%] [G loss: 1.664111]
--------------- Epoch 171 ---------------
171 [D loss: 16.118309, acc: 0%] [G loss: 1.698078]
--------------- Epoch 172 ---------------
172 [D loss: 15.543123, acc: 0%] [G loss: 1.921693]
--------------- Epoch 173 ---------------
173 [D loss: 15.820539, acc: 0%] [G loss: 1.692953]
--------------- Epoch 174 ---------------
174 [D loss: 15.910888, acc: 0%] [G loss: 1.414989]
--------------- Epoch 175 ---------------
175 [D loss: 16.406361, acc: 0%] [G loss: 1.698521]
--------------- Epoch 176 ---------------
176 [D loss: 15.685575, acc: 0%] [G loss: 1.819338]
--------------- Epoch 177 ---------------
177 [D loss: 16.115091, acc: 0%] [G loss: 1.798494]
--------------- Epoch 178 ---------------
178 [D loss: 16.181761, acc: 0%] [G loss: 1.652159]
--------------- Epoch 179 ---------------
179 [D loss: 15.942348, acc: 0%] [G loss: 1.469809]
--------------- Epoch 180 ---------------
180 [D loss: 16.035866, acc: 0%] [G loss: 1.419968]
--------------- Epoch 181 ---------------
181 [D loss: 16.197481, acc: 0%] [G loss: 1.672230]
--------------- Epoch 182 ---------------
182 [D loss: 16.077110, acc: 0%] [G loss: 1.950921]
--------------- Epoch 183 ---------------
183 [D loss: 16.238068, acc: 0%] [G loss: 1.718088]
--------------- Epoch 184 ---------------
184 [D loss: 15.506413, acc: 0%] [G loss: 1.885913]
--------------- Epoch 185 ---------------
185 [D loss: 16.064470, acc: 0%] [G loss: 1.635202]
--------------- Epoch 186 ---------------
186 [D loss: 16.050663, acc: 0%] [G loss: 1.560817]
--------------- Epoch 187 ---------------
187 [D loss: 16.369343, acc: 0%] [G loss: 1.379884]
--------------- Epoch 188 ---------------
188 [D loss: 15.402411, acc: 0%] [G loss: 1.757609]
--------------- Epoch 189 ---------------
189 [D loss: 16.439453, acc: 0%] [G loss: 1.505996]
--------------- Epoch 190 ---------------
190 [D loss: 15.865658, acc: 0%] [G loss: 1.767711]
--------------- Epoch 191 ---------------
191 [D loss: 16.088505, acc: 0%] [G loss: 1.798839]
--------------- Epoch 192 ---------------
192 [D loss: 16.028463, acc: 0%] [G loss: 1.305292]
--------------- Epoch 193 ---------------
193 [D loss: 16.121092, acc: 0%] [G loss: 1.402193]
--------------- Epoch 194 ---------------
194 [D loss: 15.529953, acc: 0%] [G loss: 1.432548]
--------------- Epoch 195 ---------------
195 [D loss: 16.281309, acc: 0%] [G loss: 1.527222]
--------------- Epoch 196 ---------------
196 [D loss: 15.957241, acc: 0%] [G loss: 1.466670]
--------------- Epoch 197 ---------------
197 [D loss: 15.861963, acc: 0%] [G loss: 1.557484]
--------------- Epoch 198 ---------------
198 [D loss: 15.645219, acc: 0%] [G loss: 1.655770]
--------------- Epoch 199 ---------------
199 [D loss: 16.112635, acc: 0%] [G loss: 1.672475]
--------------- Epoch 200 ---------------
200 [D loss: 16.146423, acc: 0%] [G loss: 1.908828]
########## N units 4 ##########
generator
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_58 (LSTM)               (None, 50, 4)             160       
_________________________________________________________________
dropout_55 (Dropout)         (None, 50, 4)             0         
_________________________________________________________________
time_distributed_32 (TimeDis (None, 50, 2)             10        
=================================================================
Total params: 170
Trainable params: 170
Non-trainable params: 0
_________________________________________________________________
None
discriminator
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_59 (LSTM)               (None, 50, 4)             112       
_________________________________________________________________
dropout_56 (Dropout)         (None, 50, 4)             0         
_________________________________________________________________
time_distributed_33 (TimeDis (None, 50, 1)             5         
_________________________________________________________________
average_pooling1d_18 (Averag (None, 1, 1)              0         
_________________________________________________________________
flatten_18 (Flatten)         (None, 1)                 0         
=================================================================
Total params: 117
Trainable params: 117
Non-trainable params: 0
_________________________________________________________________
None
GAN
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_18 (InputLayer)        (None, 50, 5)             0         
_________________________________________________________________
sequential_35 (Sequential)   (None, 50, 2)             170       
_________________________________________________________________
sequential_36 (Sequential)   (None, 1)                 117       
=================================================================
Total params: 287
Trainable params: 170
Non-trainable params: 117
_________________________________________________________________
None
--------------- Epoch 1 ---------------
1 [D loss: 16.355120, acc: 0%] [G loss: 1.814460]
--------------- Epoch 2 ---------------
2 [D loss: 15.885713, acc: 0%] [G loss: 1.778458]
--------------- Epoch 3 ---------------
3 [D loss: 15.702700, acc: 0%] [G loss: 1.475976]
--------------- Epoch 4 ---------------
4 [D loss: 15.342725, acc: 0%] [G loss: 1.678065]
--------------- Epoch 5 ---------------
5 [D loss: 16.344265, acc: 0%] [G loss: 1.293755]
--------------- Epoch 6 ---------------
6 [D loss: 16.356897, acc: 0%] [G loss: 1.673522]
--------------- Epoch 7 ---------------
7 [D loss: 15.983028, acc: 0%] [G loss: 1.554177]
--------------- Epoch 8 ---------------
8 [D loss: 15.858913, acc: 0%] [G loss: 1.643155]
--------------- Epoch 9 ---------------
9 [D loss: 16.072487, acc: 0%] [G loss: 1.524605]
--------------- Epoch 10 ---------------
10 [D loss: 15.809323, acc: 0%] [G loss: 1.487838]
--------------- Epoch 11 ---------------
11 [D loss: 16.080364, acc: 0%] [G loss: 1.585770]
--------------- Epoch 12 ---------------
12 [D loss: 15.958570, acc: 0%] [G loss: 1.422662]
--------------- Epoch 13 ---------------
13 [D loss: 15.637968, acc: 0%] [G loss: 1.762017]
--------------- Epoch 14 ---------------
14 [D loss: 16.082613, acc: 0%] [G loss: 1.582315]
--------------- Epoch 15 ---------------
15 [D loss: 16.069241, acc: 0%] [G loss: 1.413109]
--------------- Epoch 16 ---------------
16 [D loss: 16.083841, acc: 0%] [G loss: 1.915685]
--------------- Epoch 17 ---------------
17 [D loss: 16.032610, acc: 0%] [G loss: 1.681679]
--------------- Epoch 18 ---------------
18 [D loss: 15.655169, acc: 0%] [G loss: 1.407526]
--------------- Epoch 19 ---------------
19 [D loss: 15.726792, acc: 0%] [G loss: 1.636848]
--------------- Epoch 20 ---------------
20 [D loss: 15.892385, acc: 0%] [G loss: 1.366554]
--------------- Epoch 21 ---------------
21 [D loss: 15.842997, acc: 0%] [G loss: 1.256908]
--------------- Epoch 22 ---------------
22 [D loss: 15.604059, acc: 0%] [G loss: 1.392568]
--------------- Epoch 23 ---------------
23 [D loss: 15.880693, acc: 0%] [G loss: 1.560992]
--------------- Epoch 24 ---------------
24 [D loss: 16.082539, acc: 0%] [G loss: 1.461354]
--------------- Epoch 25 ---------------
25 [D loss: 15.522431, acc: 0%] [G loss: 1.691905]
--------------- Epoch 26 ---------------
26 [D loss: 15.421041, acc: 0%] [G loss: 1.437281]
--------------- Epoch 27 ---------------
27 [D loss: 15.711436, acc: 0%] [G loss: 1.443788]
--------------- Epoch 28 ---------------
28 [D loss: 15.931770, acc: 0%] [G loss: 1.205924]
--------------- Epoch 29 ---------------
29 [D loss: 15.736502, acc: 0%] [G loss: 1.623961]
--------------- Epoch 30 ---------------
30 [D loss: 15.842502, acc: 0%] [G loss: 1.643802]
--------------- Epoch 31 ---------------
31 [D loss: 16.047377, acc: 0%] [G loss: 1.600836]
--------------- Epoch 32 ---------------
32 [D loss: 16.234045, acc: 0%] [G loss: 1.675754]
--------------- Epoch 33 ---------------
33 [D loss: 15.820532, acc: 0%] [G loss: 1.657384]
--------------- Epoch 34 ---------------
34 [D loss: 16.018116, acc: 0%] [G loss: 1.556877]
--------------- Epoch 35 ---------------
35 [D loss: 15.778200, acc: 0%] [G loss: 1.512011]
--------------- Epoch 36 ---------------
36 [D loss: 15.894783, acc: 0%] [G loss: 1.526312]
--------------- Epoch 37 ---------------
37 [D loss: 15.896000, acc: 0%] [G loss: 1.729846]
--------------- Epoch 38 ---------------
38 [D loss: 15.901708, acc: 0%] [G loss: 1.449038]
--------------- Epoch 39 ---------------
39 [D loss: 15.748181, acc: 0%] [G loss: 1.791937]
--------------- Epoch 40 ---------------
40 [D loss: 15.732977, acc: 0%] [G loss: 1.487627]
--------------- Epoch 41 ---------------
41 [D loss: 16.074818, acc: 0%] [G loss: 1.444350]
--------------- Epoch 42 ---------------
42 [D loss: 15.992746, acc: 0%] [G loss: 1.416789]
--------------- Epoch 43 ---------------
43 [D loss: 15.843462, acc: 0%] [G loss: 1.501899]
--------------- Epoch 44 ---------------
44 [D loss: 15.963544, acc: 0%] [G loss: 1.703195]
--------------- Epoch 45 ---------------
45 [D loss: 15.520867, acc: 0%] [G loss: 1.772685]
--------------- Epoch 46 ---------------
46 [D loss: 15.521774, acc: 0%] [G loss: 1.815168]
--------------- Epoch 47 ---------------
47 [D loss: 15.827453, acc: 0%] [G loss: 1.508618]
--------------- Epoch 48 ---------------
48 [D loss: 15.720368, acc: 0%] [G loss: 1.631257]
--------------- Epoch 49 ---------------
49 [D loss: 15.980301, acc: 0%] [G loss: 1.716111]
--------------- Epoch 50 ---------------
50 [D loss: 15.985507, acc: 0%] [G loss: 1.800794]
--------------- Epoch 51 ---------------
51 [D loss: 16.107428, acc: 0%] [G loss: 1.655772]
--------------- Epoch 52 ---------------
52 [D loss: 16.297289, acc: 0%] [G loss: 1.499333]
--------------- Epoch 53 ---------------
53 [D loss: 16.026945, acc: 0%] [G loss: 1.241447]
--------------- Epoch 54 ---------------
54 [D loss: 15.767434, acc: 0%] [G loss: 1.532032]
--------------- Epoch 55 ---------------
55 [D loss: 15.828148, acc: 0%] [G loss: 1.607404]
--------------- Epoch 56 ---------------
56 [D loss: 15.728347, acc: 0%] [G loss: 1.602173]
--------------- Epoch 57 ---------------
57 [D loss: 15.808447, acc: 0%] [G loss: 1.736541]
--------------- Epoch 58 ---------------
58 [D loss: 16.223532, acc: 0%] [G loss: 1.443335]
--------------- Epoch 59 ---------------
59 [D loss: 15.875321, acc: 0%] [G loss: 1.809400]
--------------- Epoch 60 ---------------
60 [D loss: 16.142029, acc: 0%] [G loss: 1.407135]
--------------- Epoch 61 ---------------
61 [D loss: 15.680516, acc: 0%] [G loss: 1.827888]
--------------- Epoch 62 ---------------
62 [D loss: 16.188433, acc: 0%] [G loss: 1.405391]
--------------- Epoch 63 ---------------
63 [D loss: 15.832153, acc: 0%] [G loss: 1.415140]
--------------- Epoch 64 ---------------
64 [D loss: 15.564458, acc: 0%] [G loss: 1.338752]
--------------- Epoch 65 ---------------
65 [D loss: 15.953715, acc: 0%] [G loss: 1.445688]
--------------- Epoch 66 ---------------
66 [D loss: 15.931484, acc: 0%] [G loss: 1.575060]
--------------- Epoch 67 ---------------
67 [D loss: 15.776004, acc: 0%] [G loss: 1.540231]
--------------- Epoch 68 ---------------
68 [D loss: 16.443331, acc: 0%] [G loss: 1.834887]
--------------- Epoch 69 ---------------
69 [D loss: 15.846472, acc: 0%] [G loss: 1.816649]
--------------- Epoch 70 ---------------
70 [D loss: 16.289019, acc: 0%] [G loss: 1.630655]
--------------- Epoch 71 ---------------
71 [D loss: 16.456066, acc: 0%] [G loss: 1.488835]
--------------- Epoch 72 ---------------
72 [D loss: 15.909773, acc: 0%] [G loss: 1.523792]
--------------- Epoch 73 ---------------
73 [D loss: 16.317596, acc: 0%] [G loss: 1.729272]
--------------- Epoch 74 ---------------
74 [D loss: 16.069672, acc: 0%] [G loss: 1.749571]
--------------- Epoch 75 ---------------
75 [D loss: 16.010921, acc: 0%] [G loss: 1.669675]
--------------- Epoch 76 ---------------
76 [D loss: 15.763622, acc: 0%] [G loss: 1.745655]
--------------- Epoch 77 ---------------
77 [D loss: 16.657625, acc: 0%] [G loss: 1.597993]
--------------- Epoch 78 ---------------
78 [D loss: 15.998830, acc: 0%] [G loss: 1.429270]
--------------- Epoch 79 ---------------
79 [D loss: 15.608975, acc: 0%] [G loss: 1.521308]
--------------- Epoch 80 ---------------
80 [D loss: 16.446096, acc: 0%] [G loss: 1.624209]
--------------- Epoch 81 ---------------
81 [D loss: 15.887395, acc: 0%] [G loss: 1.548560]
--------------- Epoch 82 ---------------
82 [D loss: 15.694511, acc: 0%] [G loss: 1.841784]
--------------- Epoch 83 ---------------
83 [D loss: 15.816544, acc: 0%] [G loss: 1.562182]
--------------- Epoch 84 ---------------
84 [D loss: 16.086807, acc: 0%] [G loss: 1.691832]
--------------- Epoch 85 ---------------
85 [D loss: 15.962986, acc: 0%] [G loss: 1.492356]
--------------- Epoch 86 ---------------
86 [D loss: 15.927212, acc: 0%] [G loss: 1.588091]
--------------- Epoch 87 ---------------
87 [D loss: 15.970743, acc: 0%] [G loss: 1.718807]
--------------- Epoch 88 ---------------
88 [D loss: 16.049360, acc: 0%] [G loss: 1.486845]
--------------- Epoch 89 ---------------
89 [D loss: 15.804461, acc: 0%] [G loss: 1.561460]
--------------- Epoch 90 ---------------
90 [D loss: 15.966767, acc: 0%] [G loss: 1.630915]
--------------- Epoch 91 ---------------
91 [D loss: 16.214344, acc: 0%] [G loss: 1.621367]
--------------- Epoch 92 ---------------
92 [D loss: 15.685247, acc: 0%] [G loss: 1.685798]
--------------- Epoch 93 ---------------
93 [D loss: 15.885336, acc: 0%] [G loss: 1.726476]
--------------- Epoch 94 ---------------
94 [D loss: 15.778056, acc: 0%] [G loss: 1.827002]
--------------- Epoch 95 ---------------
95 [D loss: 15.680882, acc: 0%] [G loss: 1.525350]
--------------- Epoch 96 ---------------
96 [D loss: 15.847626, acc: 0%] [G loss: 1.789331]
--------------- Epoch 97 ---------------
97 [D loss: 15.964274, acc: 0%] [G loss: 1.564213]
--------------- Epoch 98 ---------------
98 [D loss: 16.063913, acc: 0%] [G loss: 1.639329]
--------------- Epoch 99 ---------------
99 [D loss: 15.937410, acc: 0%] [G loss: 1.699273]
--------------- Epoch 100 ---------------
100 [D loss: 15.316786, acc: 0%] [G loss: 1.602805]
--------------- Epoch 101 ---------------
101 [D loss: 15.729321, acc: 0%] [G loss: 1.736967]
--------------- Epoch 102 ---------------
102 [D loss: 15.913589, acc: 0%] [G loss: 1.362773]
--------------- Epoch 103 ---------------
103 [D loss: 15.783987, acc: 0%] [G loss: 1.532944]
--------------- Epoch 104 ---------------
104 [D loss: 16.549541, acc: 0%] [G loss: 1.412210]
--------------- Epoch 105 ---------------
105 [D loss: 16.051456, acc: 0%] [G loss: 1.552373]
--------------- Epoch 106 ---------------
106 [D loss: 15.770281, acc: 0%] [G loss: 1.581112]
--------------- Epoch 107 ---------------
107 [D loss: 16.147322, acc: 0%] [G loss: 1.657568]
--------------- Epoch 108 ---------------
108 [D loss: 16.214870, acc: 0%] [G loss: 1.326982]
--------------- Epoch 109 ---------------
109 [D loss: 15.731587, acc: 0%] [G loss: 1.722208]
--------------- Epoch 110 ---------------
110 [D loss: 16.366739, acc: 0%] [G loss: 1.782437]
--------------- Epoch 111 ---------------
111 [D loss: 15.898847, acc: 0%] [G loss: 1.688800]
--------------- Epoch 112 ---------------
112 [D loss: 16.413946, acc: 0%] [G loss: 1.945495]
--------------- Epoch 113 ---------------
113 [D loss: 15.794454, acc: 0%] [G loss: 1.716734]
--------------- Epoch 114 ---------------
114 [D loss: 15.733102, acc: 0%] [G loss: 1.448102]
--------------- Epoch 115 ---------------
115 [D loss: 16.049757, acc: 0%] [G loss: 1.605689]
--------------- Epoch 116 ---------------
116 [D loss: 16.351021, acc: 0%] [G loss: 1.562716]
--------------- Epoch 117 ---------------
117 [D loss: 15.447493, acc: 0%] [G loss: 1.593736]
--------------- Epoch 118 ---------------
118 [D loss: 16.380306, acc: 0%] [G loss: 1.863103]
--------------- Epoch 119 ---------------
119 [D loss: 15.855697, acc: 0%] [G loss: 1.440601]
--------------- Epoch 120 ---------------
120 [D loss: 15.925147, acc: 0%] [G loss: 1.503540]
--------------- Epoch 121 ---------------
121 [D loss: 15.675756, acc: 0%] [G loss: 1.728369]
--------------- Epoch 122 ---------------
122 [D loss: 15.972914, acc: 0%] [G loss: 1.935651]
--------------- Epoch 123 ---------------
123 [D loss: 15.672054, acc: 0%] [G loss: 1.196882]
--------------- Epoch 124 ---------------
124 [D loss: 15.864487, acc: 0%] [G loss: 1.726884]
--------------- Epoch 125 ---------------
125 [D loss: 15.797143, acc: 0%] [G loss: 1.618165]
--------------- Epoch 126 ---------------
126 [D loss: 15.972320, acc: 0%] [G loss: 1.631541]
--------------- Epoch 127 ---------------
127 [D loss: 16.240685, acc: 0%] [G loss: 1.351932]
--------------- Epoch 128 ---------------
128 [D loss: 16.261084, acc: 0%] [G loss: 1.543510]
--------------- Epoch 129 ---------------
129 [D loss: 16.146885, acc: 0%] [G loss: 1.687831]
--------------- Epoch 130 ---------------
130 [D loss: 15.799598, acc: 0%] [G loss: 1.855245]
--------------- Epoch 131 ---------------
131 [D loss: 16.257908, acc: 0%] [G loss: 1.758172]
--------------- Epoch 132 ---------------
132 [D loss: 15.982866, acc: 0%] [G loss: 1.526405]
--------------- Epoch 133 ---------------
133 [D loss: 15.703715, acc: 0%] [G loss: 1.668433]
--------------- Epoch 134 ---------------
134 [D loss: 15.857149, acc: 0%] [G loss: 1.474374]
--------------- Epoch 135 ---------------
135 [D loss: 16.385170, acc: 0%] [G loss: 1.613851]
--------------- Epoch 136 ---------------
136 [D loss: 15.914593, acc: 0%] [G loss: 1.464496]
--------------- Epoch 137 ---------------
137 [D loss: 16.067228, acc: 0%] [G loss: 1.530765]
--------------- Epoch 138 ---------------
138 [D loss: 15.655984, acc: 0%] [G loss: 1.743528]
--------------- Epoch 139 ---------------
139 [D loss: 15.813793, acc: 0%] [G loss: 1.583974]
--------------- Epoch 140 ---------------
140 [D loss: 16.065954, acc: 0%] [G loss: 1.756904]
--------------- Epoch 141 ---------------
141 [D loss: 16.054783, acc: 0%] [G loss: 1.649953]
--------------- Epoch 142 ---------------
142 [D loss: 16.108013, acc: 0%] [G loss: 1.572750]
--------------- Epoch 143 ---------------
143 [D loss: 16.250832, acc: 0%] [G loss: 1.670592]
--------------- Epoch 144 ---------------
144 [D loss: 15.549457, acc: 0%] [G loss: 1.535808]
--------------- Epoch 145 ---------------
145 [D loss: 16.003542, acc: 0%] [G loss: 1.349683]
--------------- Epoch 146 ---------------
146 [D loss: 15.730850, acc: 0%] [G loss: 1.731495]
--------------- Epoch 147 ---------------
147 [D loss: 15.799574, acc: 0%] [G loss: 1.460245]
--------------- Epoch 148 ---------------
148 [D loss: 15.976088, acc: 0%] [G loss: 1.756448]
--------------- Epoch 149 ---------------
149 [D loss: 15.949119, acc: 0%] [G loss: 1.515863]
--------------- Epoch 150 ---------------
150 [D loss: 15.776434, acc: 0%] [G loss: 1.752876]
--------------- Epoch 151 ---------------
151 [D loss: 16.139723, acc: 0%] [G loss: 1.363797]
--------------- Epoch 152 ---------------
152 [D loss: 16.071529, acc: 0%] [G loss: 1.608806]
--------------- Epoch 153 ---------------
153 [D loss: 15.903890, acc: 0%] [G loss: 1.326373]
--------------- Epoch 154 ---------------
154 [D loss: 15.799172, acc: 0%] [G loss: 1.699111]
--------------- Epoch 155 ---------------
155 [D loss: 15.811212, acc: 0%] [G loss: 1.652412]
--------------- Epoch 156 ---------------
156 [D loss: 16.055931, acc: 0%] [G loss: 1.848974]
--------------- Epoch 157 ---------------
157 [D loss: 16.341379, acc: 0%] [G loss: 1.729327]
--------------- Epoch 158 ---------------
158 [D loss: 15.876120, acc: 0%] [G loss: 1.405512]
--------------- Epoch 159 ---------------
159 [D loss: 16.360638, acc: 0%] [G loss: 1.530502]
--------------- Epoch 160 ---------------
160 [D loss: 16.141235, acc: 0%] [G loss: 1.668757]
--------------- Epoch 161 ---------------
161 [D loss: 15.662848, acc: 0%] [G loss: 1.565931]
--------------- Epoch 162 ---------------
162 [D loss: 16.153122, acc: 0%] [G loss: 1.466202]
--------------- Epoch 163 ---------------
163 [D loss: 15.664349, acc: 0%] [G loss: 1.644590]
--------------- Epoch 164 ---------------
164 [D loss: 16.068827, acc: 0%] [G loss: 1.274432]
--------------- Epoch 165 ---------------
165 [D loss: 15.904957, acc: 0%] [G loss: 1.507339]
--------------- Epoch 166 ---------------
166 [D loss: 16.237265, acc: 0%] [G loss: 1.541541]
--------------- Epoch 167 ---------------
167 [D loss: 16.111155, acc: 0%] [G loss: 1.361709]
--------------- Epoch 168 ---------------
168 [D loss: 15.563532, acc: 0%] [G loss: 1.482035]
--------------- Epoch 169 ---------------
169 [D loss: 16.230152, acc: 0%] [G loss: 1.413161]
--------------- Epoch 170 ---------------
170 [D loss: 16.467129, acc: 0%] [G loss: 1.746057]
--------------- Epoch 171 ---------------
171 [D loss: 15.788177, acc: 0%] [G loss: 1.495307]
--------------- Epoch 172 ---------------
172 [D loss: 15.962108, acc: 0%] [G loss: 1.657750]
--------------- Epoch 173 ---------------
173 [D loss: 15.854675, acc: 0%] [G loss: 1.516387]
--------------- Epoch 174 ---------------
174 [D loss: 15.731256, acc: 0%] [G loss: 1.616036]
--------------- Epoch 175 ---------------
175 [D loss: 15.903933, acc: 0%] [G loss: 1.457659]
--------------- Epoch 176 ---------------
176 [D loss: 15.955573, acc: 0%] [G loss: 1.611738]
--------------- Epoch 177 ---------------
177 [D loss: 16.036066, acc: 0%] [G loss: 2.039540]
--------------- Epoch 178 ---------------
178 [D loss: 16.355873, acc: 0%] [G loss: 1.628508]
--------------- Epoch 179 ---------------
179 [D loss: 15.867373, acc: 0%] [G loss: 1.421464]
--------------- Epoch 180 ---------------
180 [D loss: 16.298250, acc: 0%] [G loss: 1.418634]
--------------- Epoch 181 ---------------
181 [D loss: 15.956868, acc: 0%] [G loss: 1.370369]
--------------- Epoch 182 ---------------
182 [D loss: 16.327772, acc: 0%] [G loss: 1.643640]
--------------- Epoch 183 ---------------
183 [D loss: 15.918966, acc: 0%] [G loss: 1.119774]
--------------- Epoch 184 ---------------
184 [D loss: 16.081381, acc: 0%] [G loss: 1.414834]
--------------- Epoch 185 ---------------
185 [D loss: 15.533664, acc: 0%] [G loss: 1.622836]
--------------- Epoch 186 ---------------
186 [D loss: 16.181440, acc: 0%] [G loss: 1.487777]
--------------- Epoch 187 ---------------
187 [D loss: 15.577910, acc: 0%] [G loss: 1.565601]
--------------- Epoch 188 ---------------
188 [D loss: 15.972589, acc: 0%] [G loss: 1.595383]
--------------- Epoch 189 ---------------
189 [D loss: 15.348717, acc: 0%] [G loss: 1.865746]
--------------- Epoch 190 ---------------
190 [D loss: 15.642968, acc: 0%] [G loss: 1.861328]
--------------- Epoch 191 ---------------
191 [D loss: 16.051455, acc: 0%] [G loss: 1.669194]
--------------- Epoch 192 ---------------
192 [D loss: 16.250612, acc: 0%] [G loss: 1.557063]
--------------- Epoch 193 ---------------
193 [D loss: 15.975104, acc: 0%] [G loss: 1.574857]
--------------- Epoch 194 ---------------
194 [D loss: 15.685685, acc: 0%] [G loss: 1.440807]
--------------- Epoch 195 ---------------
195 [D loss: 16.101292, acc: 0%] [G loss: 1.889536]
--------------- Epoch 196 ---------------
196 [D loss: 16.049273, acc: 0%] [G loss: 1.627507]
--------------- Epoch 197 ---------------
197 [D loss: 15.890452, acc: 0%] [G loss: 1.588110]
--------------- Epoch 198 ---------------
198 [D loss: 15.811005, acc: 0%] [G loss: 1.822914]
--------------- Epoch 199 ---------------
199 [D loss: 16.026089, acc: 0%] [G loss: 1.644084]
--------------- Epoch 200 ---------------
200 [D loss: 15.699904, acc: 0%] [G loss: 1.594372]
########## N units 8 ##########
generator
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_60 (LSTM)               (None, 50, 8)             448       
_________________________________________________________________
dropout_57 (Dropout)         (None, 50, 8)             0         
_________________________________________________________________
time_distributed_34 (TimeDis (None, 50, 2)             18        
=================================================================
Total params: 466
Trainable params: 466
Non-trainable params: 0
_________________________________________________________________
None
discriminator
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_61 (LSTM)               (None, 50, 8)             352       
_________________________________________________________________
dropout_58 (Dropout)         (None, 50, 8)             0         
_________________________________________________________________
time_distributed_35 (TimeDis (None, 50, 1)             9         
_________________________________________________________________
average_pooling1d_19 (Averag (None, 1, 1)              0         
_________________________________________________________________
flatten_19 (Flatten)         (None, 1)                 0         
=================================================================
Total params: 361
Trainable params: 361
Non-trainable params: 0
_________________________________________________________________
None
GAN
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_19 (InputLayer)        (None, 50, 5)             0         
_________________________________________________________________
sequential_37 (Sequential)   (None, 50, 2)             466       
_________________________________________________________________
sequential_38 (Sequential)   (None, 1)                 361       
=================================================================
Total params: 827
Trainable params: 466
Non-trainable params: 361
_________________________________________________________________
None
--------------- Epoch 1 ---------------
1 [D loss: 16.068523, acc: 0%] [G loss: 1.511986]
--------------- Epoch 2 ---------------
2 [D loss: 15.875296, acc: 0%] [G loss: 1.538797]
--------------- Epoch 3 ---------------
3 [D loss: 15.672616, acc: 0%] [G loss: 1.587683]
--------------- Epoch 4 ---------------
4 [D loss: 16.223209, acc: 0%] [G loss: 1.562070]
--------------- Epoch 5 ---------------
5 [D loss: 15.780662, acc: 0%] [G loss: 1.286369]
--------------- Epoch 6 ---------------
6 [D loss: 16.120306, acc: 0%] [G loss: 1.725591]
--------------- Epoch 7 ---------------
7 [D loss: 16.035919, acc: 0%] [G loss: 1.875308]
--------------- Epoch 8 ---------------
8 [D loss: 15.899560, acc: 0%] [G loss: 2.031976]
--------------- Epoch 9 ---------------
9 [D loss: 15.933397, acc: 0%] [G loss: 1.416320]
--------------- Epoch 10 ---------------
10 [D loss: 15.834480, acc: 0%] [G loss: 1.521246]
--------------- Epoch 11 ---------------
11 [D loss: 16.025002, acc: 0%] [G loss: 1.450714]
--------------- Epoch 12 ---------------
12 [D loss: 15.598192, acc: 0%] [G loss: 1.320845]
--------------- Epoch 13 ---------------
13 [D loss: 15.807251, acc: 0%] [G loss: 1.818233]
--------------- Epoch 14 ---------------
14 [D loss: 16.247601, acc: 0%] [G loss: 1.464720]
--------------- Epoch 15 ---------------
15 [D loss: 16.407959, acc: 0%] [G loss: 1.866647]
--------------- Epoch 16 ---------------
16 [D loss: 16.398468, acc: 0%] [G loss: 1.543110]
--------------- Epoch 17 ---------------
17 [D loss: 16.284662, acc: 0%] [G loss: 1.663562]
--------------- Epoch 18 ---------------
18 [D loss: 16.164110, acc: 0%] [G loss: 1.844124]
--------------- Epoch 19 ---------------
19 [D loss: 15.647630, acc: 0%] [G loss: 1.512761]
--------------- Epoch 20 ---------------
20 [D loss: 15.823670, acc: 0%] [G loss: 1.634329]
--------------- Epoch 21 ---------------
21 [D loss: 15.835176, acc: 0%] [G loss: 1.353255]
--------------- Epoch 22 ---------------
22 [D loss: 16.075550, acc: 0%] [G loss: 1.847310]
--------------- Epoch 23 ---------------
23 [D loss: 16.059141, acc: 0%] [G loss: 1.848006]
--------------- Epoch 24 ---------------
24 [D loss: 16.169222, acc: 0%] [G loss: 1.711251]
--------------- Epoch 25 ---------------
25 [D loss: 15.942238, acc: 0%] [G loss: 1.464640]
--------------- Epoch 26 ---------------
26 [D loss: 16.038540, acc: 0%] [G loss: 1.450897]
--------------- Epoch 27 ---------------
27 [D loss: 16.052654, acc: 0%] [G loss: 1.599646]
--------------- Epoch 28 ---------------
28 [D loss: 16.115032, acc: 0%] [G loss: 1.586840]
--------------- Epoch 29 ---------------
29 [D loss: 15.644703, acc: 0%] [G loss: 1.822035]
--------------- Epoch 30 ---------------
30 [D loss: 15.987165, acc: 0%] [G loss: 1.666212]
--------------- Epoch 31 ---------------
31 [D loss: 15.711650, acc: 0%] [G loss: 1.557869]
--------------- Epoch 32 ---------------
32 [D loss: 15.784402, acc: 0%] [G loss: 1.593457]
--------------- Epoch 33 ---------------
33 [D loss: 15.769219, acc: 0%] [G loss: 1.794264]
--------------- Epoch 34 ---------------
34 [D loss: 16.324675, acc: 0%] [G loss: 1.557097]
--------------- Epoch 35 ---------------
35 [D loss: 15.738944, acc: 0%] [G loss: 1.561888]
--------------- Epoch 36 ---------------
36 [D loss: 16.018753, acc: 0%] [G loss: 1.495960]
--------------- Epoch 37 ---------------
37 [D loss: 15.825685, acc: 0%] [G loss: 1.453666]
--------------- Epoch 38 ---------------
38 [D loss: 15.788954, acc: 0%] [G loss: 1.798302]
--------------- Epoch 39 ---------------
39 [D loss: 16.224762, acc: 0%] [G loss: 1.315024]
--------------- Epoch 40 ---------------
40 [D loss: 15.779748, acc: 0%] [G loss: 1.514781]
--------------- Epoch 41 ---------------
41 [D loss: 16.126400, acc: 0%] [G loss: 1.466103]
--------------- Epoch 42 ---------------
42 [D loss: 16.188307, acc: 0%] [G loss: 1.412551]
--------------- Epoch 43 ---------------
43 [D loss: 15.961331, acc: 0%] [G loss: 1.492469]
--------------- Epoch 44 ---------------
44 [D loss: 15.377103, acc: 0%] [G loss: 1.534195]
--------------- Epoch 45 ---------------
45 [D loss: 15.938719, acc: 0%] [G loss: 1.557578]
--------------- Epoch 46 ---------------
46 [D loss: 15.849633, acc: 0%] [G loss: 1.630280]
--------------- Epoch 47 ---------------
47 [D loss: 15.717019, acc: 0%] [G loss: 1.602840]
--------------- Epoch 48 ---------------
48 [D loss: 16.152714, acc: 0%] [G loss: 1.695898]
--------------- Epoch 49 ---------------
49 [D loss: 15.944968, acc: 0%] [G loss: 1.277634]
--------------- Epoch 50 ---------------
50 [D loss: 15.825646, acc: 0%] [G loss: 1.676467]
--------------- Epoch 51 ---------------
51 [D loss: 15.654469, acc: 0%] [G loss: 1.640069]
--------------- Epoch 52 ---------------
52 [D loss: 15.694880, acc: 0%] [G loss: 1.704369]
--------------- Epoch 53 ---------------
53 [D loss: 15.870326, acc: 0%] [G loss: 1.562527]
--------------- Epoch 54 ---------------
54 [D loss: 16.294598, acc: 0%] [G loss: 1.612864]
--------------- Epoch 55 ---------------
55 [D loss: 15.823370, acc: 0%] [G loss: 1.616567]
--------------- Epoch 56 ---------------
56 [D loss: 15.891415, acc: 0%] [G loss: 1.519933]
--------------- Epoch 57 ---------------
57 [D loss: 16.064240, acc: 0%] [G loss: 1.848280]
--------------- Epoch 58 ---------------
58 [D loss: 15.943722, acc: 0%] [G loss: 1.178184]
--------------- Epoch 59 ---------------
59 [D loss: 16.298328, acc: 0%] [G loss: 1.501541]
--------------- Epoch 60 ---------------
60 [D loss: 16.118023, acc: 0%] [G loss: 1.408483]
--------------- Epoch 61 ---------------
61 [D loss: 16.094109, acc: 0%] [G loss: 1.644603]
--------------- Epoch 62 ---------------
62 [D loss: 16.245459, acc: 0%] [G loss: 1.641929]
--------------- Epoch 63 ---------------
63 [D loss: 15.771560, acc: 0%] [G loss: 1.415523]
--------------- Epoch 64 ---------------
64 [D loss: 16.072824, acc: 0%] [G loss: 1.762271]
--------------- Epoch 65 ---------------
65 [D loss: 15.626390, acc: 0%] [G loss: 1.350595]
--------------- Epoch 66 ---------------
66 [D loss: 16.135691, acc: 0%] [G loss: 1.641337]
--------------- Epoch 67 ---------------
67 [D loss: 15.932797, acc: 0%] [G loss: 1.803150]
--------------- Epoch 68 ---------------
68 [D loss: 16.660748, acc: 0%] [G loss: 1.745241]
--------------- Epoch 69 ---------------
69 [D loss: 16.429029, acc: 0%] [G loss: 1.555005]
--------------- Epoch 70 ---------------
70 [D loss: 15.848288, acc: 0%] [G loss: 1.861505]
--------------- Epoch 71 ---------------
71 [D loss: 15.754863, acc: 0%] [G loss: 1.694423]
--------------- Epoch 72 ---------------
72 [D loss: 16.009066, acc: 0%] [G loss: 1.632351]
--------------- Epoch 73 ---------------
73 [D loss: 16.024210, acc: 0%] [G loss: 1.607228]
--------------- Epoch 74 ---------------
74 [D loss: 15.328328, acc: 0%] [G loss: 1.773471]
--------------- Epoch 75 ---------------
75 [D loss: 16.333130, acc: 0%] [G loss: 1.590367]
--------------- Epoch 76 ---------------
76 [D loss: 16.333677, acc: 0%] [G loss: 1.708642]
--------------- Epoch 77 ---------------
77 [D loss: 15.977455, acc: 0%] [G loss: 1.552239]
--------------- Epoch 78 ---------------
78 [D loss: 16.045626, acc: 0%] [G loss: 1.595876]
--------------- Epoch 79 ---------------
79 [D loss: 15.949694, acc: 0%] [G loss: 1.512318]
--------------- Epoch 80 ---------------
80 [D loss: 16.259878, acc: 0%] [G loss: 1.668971]
--------------- Epoch 81 ---------------
81 [D loss: 16.426929, acc: 0%] [G loss: 1.449107]
--------------- Epoch 82 ---------------
82 [D loss: 15.744026, acc: 0%] [G loss: 1.227162]
--------------- Epoch 83 ---------------
83 [D loss: 15.506114, acc: 0%] [G loss: 1.553817]
--------------- Epoch 84 ---------------
84 [D loss: 15.453319, acc: 0%] [G loss: 1.574157]
--------------- Epoch 85 ---------------
85 [D loss: 15.422522, acc: 0%] [G loss: 1.587333]
--------------- Epoch 86 ---------------
86 [D loss: 15.656941, acc: 0%] [G loss: 1.712736]
--------------- Epoch 87 ---------------
87 [D loss: 15.952731, acc: 0%] [G loss: 1.596984]
--------------- Epoch 88 ---------------
88 [D loss: 16.173292, acc: 0%] [G loss: 1.472185]
--------------- Epoch 89 ---------------
89 [D loss: 16.025558, acc: 0%] [G loss: 1.482153]
--------------- Epoch 90 ---------------
90 [D loss: 16.101221, acc: 0%] [G loss: 1.350537]
--------------- Epoch 91 ---------------
91 [D loss: 16.023954, acc: 0%] [G loss: 1.301898]
--------------- Epoch 92 ---------------
92 [D loss: 16.130444, acc: 0%] [G loss: 1.556361]
--------------- Epoch 93 ---------------
93 [D loss: 16.265495, acc: 0%] [G loss: 1.478712]
--------------- Epoch 94 ---------------
94 [D loss: 15.712720, acc: 0%] [G loss: 1.838559]
--------------- Epoch 95 ---------------
95 [D loss: 15.645123, acc: 0%] [G loss: 1.576984]
--------------- Epoch 96 ---------------
96 [D loss: 16.402138, acc: 0%] [G loss: 1.668336]
--------------- Epoch 97 ---------------
97 [D loss: 16.074524, acc: 0%] [G loss: 1.940497]
--------------- Epoch 98 ---------------
98 [D loss: 15.669566, acc: 0%] [G loss: 1.482445]
--------------- Epoch 99 ---------------
99 [D loss: 15.635914, acc: 0%] [G loss: 1.626511]
--------------- Epoch 100 ---------------
100 [D loss: 15.786636, acc: 0%] [G loss: 1.737511]
--------------- Epoch 101 ---------------
101 [D loss: 16.203007, acc: 0%] [G loss: 1.479041]
--------------- Epoch 102 ---------------
102 [D loss: 16.001593, acc: 0%] [G loss: 1.575420]
--------------- Epoch 103 ---------------
103 [D loss: 15.578281, acc: 0%] [G loss: 1.811943]
--------------- Epoch 104 ---------------
104 [D loss: 15.233114, acc: 0%] [G loss: 1.537146]
--------------- Epoch 105 ---------------
105 [D loss: 15.675652, acc: 0%] [G loss: 1.505541]
--------------- Epoch 106 ---------------
106 [D loss: 16.213984, acc: 0%] [G loss: 1.545925]
--------------- Epoch 107 ---------------
107 [D loss: 15.900983, acc: 0%] [G loss: 1.391055]
--------------- Epoch 108 ---------------
108 [D loss: 15.661230, acc: 0%] [G loss: 1.900498]
--------------- Epoch 109 ---------------
109 [D loss: 15.760826, acc: 0%] [G loss: 1.275480]
--------------- Epoch 110 ---------------
110 [D loss: 16.312220, acc: 0%] [G loss: 1.718870]
--------------- Epoch 111 ---------------
111 [D loss: 15.912663, acc: 0%] [G loss: 1.897087]
--------------- Epoch 112 ---------------
112 [D loss: 16.124252, acc: 0%] [G loss: 1.489677]
--------------- Epoch 113 ---------------
113 [D loss: 16.116482, acc: 0%] [G loss: 1.415087]
--------------- Epoch 114 ---------------
114 [D loss: 15.869343, acc: 0%] [G loss: 1.686879]
--------------- Epoch 115 ---------------
115 [D loss: 16.467516, acc: 0%] [G loss: 1.785355]
--------------- Epoch 116 ---------------
116 [D loss: 15.746632, acc: 0%] [G loss: 1.490502]
--------------- Epoch 117 ---------------
117 [D loss: 15.955652, acc: 0%] [G loss: 1.482315]
--------------- Epoch 118 ---------------
118 [D loss: 15.836317, acc: 0%] [G loss: 1.606019]
--------------- Epoch 119 ---------------
119 [D loss: 16.392841, acc: 0%] [G loss: 1.448553]
--------------- Epoch 120 ---------------
120 [D loss: 15.553125, acc: 0%] [G loss: 1.739686]
--------------- Epoch 121 ---------------
121 [D loss: 15.705124, acc: 0%] [G loss: 1.374906]
--------------- Epoch 122 ---------------
122 [D loss: 16.819839, acc: 0%] [G loss: 1.752774]
--------------- Epoch 123 ---------------
123 [D loss: 15.607718, acc: 0%] [G loss: 1.484021]
--------------- Epoch 124 ---------------
124 [D loss: 15.747031, acc: 0%] [G loss: 1.706016]
--------------- Epoch 125 ---------------
125 [D loss: 16.025118, acc: 0%] [G loss: 1.609194]
--------------- Epoch 126 ---------------
126 [D loss: 16.114956, acc: 0%] [G loss: 1.712559]
--------------- Epoch 127 ---------------
127 [D loss: 16.294960, acc: 0%] [G loss: 1.566043]
--------------- Epoch 128 ---------------
128 [D loss: 15.944510, acc: 0%] [G loss: 1.569847]
--------------- Epoch 129 ---------------
129 [D loss: 15.583043, acc: 0%] [G loss: 1.666329]
--------------- Epoch 130 ---------------
130 [D loss: 15.688251, acc: 0%] [G loss: 1.529257]
--------------- Epoch 131 ---------------
131 [D loss: 16.103008, acc: 0%] [G loss: 1.658401]
--------------- Epoch 132 ---------------
132 [D loss: 16.270229, acc: 0%] [G loss: 1.517332]
--------------- Epoch 133 ---------------
133 [D loss: 15.940175, acc: 0%] [G loss: 1.679951]
--------------- Epoch 134 ---------------
134 [D loss: 15.844067, acc: 0%] [G loss: 1.788444]
--------------- Epoch 135 ---------------
135 [D loss: 15.991847, acc: 0%] [G loss: 1.499970]
--------------- Epoch 136 ---------------
136 [D loss: 16.057201, acc: 0%] [G loss: 1.324514]
--------------- Epoch 137 ---------------
137 [D loss: 15.830442, acc: 0%] [G loss: 1.532144]
--------------- Epoch 138 ---------------
138 [D loss: 15.823495, acc: 0%] [G loss: 1.738542]
--------------- Epoch 139 ---------------
139 [D loss: 16.043362, acc: 0%] [G loss: 1.526759]
--------------- Epoch 140 ---------------
140 [D loss: 16.118217, acc: 0%] [G loss: 1.571669]
--------------- Epoch 141 ---------------
141 [D loss: 16.150789, acc: 0%] [G loss: 1.769289]
--------------- Epoch 142 ---------------
142 [D loss: 15.700511, acc: 0%] [G loss: 1.599086]
--------------- Epoch 143 ---------------
143 [D loss: 15.900277, acc: 0%] [G loss: 1.356313]
--------------- Epoch 144 ---------------
144 [D loss: 15.982825, acc: 0%] [G loss: 1.665449]
--------------- Epoch 145 ---------------
145 [D loss: 15.827673, acc: 0%] [G loss: 1.483434]
--------------- Epoch 146 ---------------
146 [D loss: 15.713967, acc: 0%] [G loss: 1.845520]
--------------- Epoch 147 ---------------
147 [D loss: 15.982794, acc: 0%] [G loss: 1.761428]
--------------- Epoch 148 ---------------
148 [D loss: 15.685037, acc: 0%] [G loss: 1.562754]
--------------- Epoch 149 ---------------
149 [D loss: 16.073872, acc: 0%] [G loss: 1.527406]
--------------- Epoch 150 ---------------
150 [D loss: 16.018778, acc: 0%] [G loss: 1.528733]
--------------- Epoch 151 ---------------
151 [D loss: 15.777444, acc: 0%] [G loss: 1.474833]
--------------- Epoch 152 ---------------
152 [D loss: 15.745695, acc: 0%] [G loss: 1.575487]
--------------- Epoch 153 ---------------
153 [D loss: 16.074100, acc: 0%] [G loss: 1.583672]
--------------- Epoch 154 ---------------
154 [D loss: 16.212269, acc: 0%] [G loss: 1.566013]
--------------- Epoch 155 ---------------
155 [D loss: 15.774660, acc: 0%] [G loss: 1.556597]
--------------- Epoch 156 ---------------
156 [D loss: 15.791885, acc: 0%] [G loss: 1.543095]
--------------- Epoch 157 ---------------
157 [D loss: 15.522412, acc: 0%] [G loss: 1.658551]
--------------- Epoch 158 ---------------
158 [D loss: 16.217030, acc: 0%] [G loss: 1.813503]
--------------- Epoch 159 ---------------
159 [D loss: 16.250757, acc: 0%] [G loss: 1.689421]
--------------- Epoch 160 ---------------
160 [D loss: 15.705185, acc: 0%] [G loss: 1.900723]
--------------- Epoch 161 ---------------
161 [D loss: 15.771817, acc: 0%] [G loss: 1.671826]
--------------- Epoch 162 ---------------
162 [D loss: 15.816644, acc: 0%] [G loss: 1.434016]
--------------- Epoch 163 ---------------
163 [D loss: 15.984035, acc: 0%] [G loss: 1.595942]
--------------- Epoch 164 ---------------
164 [D loss: 15.534586, acc: 0%] [G loss: 1.476131]
--------------- Epoch 165 ---------------
165 [D loss: 15.717141, acc: 0%] [G loss: 1.221388]
--------------- Epoch 166 ---------------
166 [D loss: 16.054968, acc: 0%] [G loss: 1.942191]
--------------- Epoch 167 ---------------
167 [D loss: 16.415405, acc: 0%] [G loss: 1.531801]
--------------- Epoch 168 ---------------
168 [D loss: 15.742938, acc: 0%] [G loss: 1.621574]
--------------- Epoch 169 ---------------
169 [D loss: 15.690299, acc: 0%] [G loss: 1.600067]
--------------- Epoch 170 ---------------
170 [D loss: 16.110062, acc: 0%] [G loss: 1.525374]
--------------- Epoch 171 ---------------
171 [D loss: 15.519858, acc: 0%] [G loss: 1.688321]
--------------- Epoch 172 ---------------
172 [D loss: 16.197887, acc: 0%] [G loss: 1.568962]
--------------- Epoch 173 ---------------
173 [D loss: 15.658637, acc: 0%] [G loss: 1.675298]
--------------- Epoch 174 ---------------
174 [D loss: 16.121439, acc: 0%] [G loss: 1.787338]
--------------- Epoch 175 ---------------
175 [D loss: 16.227486, acc: 0%] [G loss: 1.543301]
--------------- Epoch 176 ---------------
176 [D loss: 16.334909, acc: 0%] [G loss: 1.746160]
--------------- Epoch 177 ---------------
177 [D loss: 15.891551, acc: 0%] [G loss: 1.421275]
--------------- Epoch 178 ---------------
178 [D loss: 16.116007, acc: 0%] [G loss: 1.713458]
--------------- Epoch 179 ---------------
179 [D loss: 15.976489, acc: 0%] [G loss: 1.312873]
--------------- Epoch 180 ---------------
180 [D loss: 16.009157, acc: 0%] [G loss: 1.440066]
--------------- Epoch 181 ---------------
181 [D loss: 15.763421, acc: 0%] [G loss: 1.371213]
--------------- Epoch 182 ---------------
182 [D loss: 16.313457, acc: 0%] [G loss: 1.694319]
--------------- Epoch 183 ---------------
183 [D loss: 16.533390, acc: 0%] [G loss: 1.545943]
--------------- Epoch 184 ---------------
184 [D loss: 16.224430, acc: 0%] [G loss: 1.652375]
--------------- Epoch 185 ---------------
185 [D loss: 15.648010, acc: 0%] [G loss: 1.912927]
--------------- Epoch 186 ---------------
186 [D loss: 16.205925, acc: 0%] [G loss: 1.899100]
--------------- Epoch 187 ---------------
187 [D loss: 15.896376, acc: 0%] [G loss: 1.596020]
--------------- Epoch 188 ---------------
188 [D loss: 15.503967, acc: 0%] [G loss: 1.799671]
--------------- Epoch 189 ---------------
189 [D loss: 16.231495, acc: 0%] [G loss: 1.450812]
--------------- Epoch 190 ---------------
190 [D loss: 15.910720, acc: 0%] [G loss: 1.511322]
--------------- Epoch 191 ---------------
191 [D loss: 15.700500, acc: 0%] [G loss: 1.527380]
--------------- Epoch 192 ---------------
192 [D loss: 16.056379, acc: 0%] [G loss: 1.555240]
--------------- Epoch 193 ---------------
193 [D loss: 15.707390, acc: 0%] [G loss: 1.441534]
--------------- Epoch 194 ---------------
194 [D loss: 15.607167, acc: 0%] [G loss: 1.423404]
--------------- Epoch 195 ---------------
195 [D loss: 16.148533, acc: 0%] [G loss: 1.599354]
--------------- Epoch 196 ---------------
196 [D loss: 16.089453, acc: 0%] [G loss: 1.827307]
--------------- Epoch 197 ---------------
197 [D loss: 16.082850, acc: 0%] [G loss: 1.907995]
--------------- Epoch 198 ---------------
198 [D loss: 15.921199, acc: 0%] [G loss: 1.905458]
--------------- Epoch 199 ---------------
199 [D loss: 15.899825, acc: 0%] [G loss: 1.477115]
--------------- Epoch 200 ---------------
200 [D loss: 15.728117, acc: 0%] [G loss: 1.828607]
########## N units 16 ##########
generator
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_62 (LSTM)               (None, 50, 16)            1408      
_________________________________________________________________
dropout_59 (Dropout)         (None, 50, 16)            0         
_________________________________________________________________
time_distributed_36 (TimeDis (None, 50, 2)             34        
=================================================================
Total params: 1,442
Trainable params: 1,442
Non-trainable params: 0
_________________________________________________________________
None
discriminator
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_63 (LSTM)               (None, 50, 16)            1216      
_________________________________________________________________
dropout_60 (Dropout)         (None, 50, 16)            0         
_________________________________________________________________
time_distributed_37 (TimeDis (None, 50, 1)             17        
_________________________________________________________________
average_pooling1d_20 (Averag (None, 1, 1)              0         
_________________________________________________________________
flatten_20 (Flatten)         (None, 1)                 0         
=================================================================
Total params: 1,233
Trainable params: 1,233
Non-trainable params: 0
_________________________________________________________________
None
GAN
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_20 (InputLayer)        (None, 50, 5)             0         
_________________________________________________________________
sequential_39 (Sequential)   (None, 50, 2)             1442      
_________________________________________________________________
sequential_40 (Sequential)   (None, 1)                 1233      
=================================================================
Total params: 2,675
Trainable params: 1,442
Non-trainable params: 1,233
_________________________________________________________________
None
--------------- Epoch 1 ---------------
1 [D loss: 15.526864, acc: 0%] [G loss: 1.571853]
--------------- Epoch 2 ---------------
2 [D loss: 16.499128, acc: 0%] [G loss: 1.701134]
--------------- Epoch 3 ---------------
3 [D loss: 16.027420, acc: 0%] [G loss: 1.690939]
--------------- Epoch 4 ---------------
4 [D loss: 15.742685, acc: 0%] [G loss: 1.435235]
--------------- Epoch 5 ---------------
5 [D loss: 15.844975, acc: 0%] [G loss: 1.612460]
--------------- Epoch 6 ---------------
6 [D loss: 16.133757, acc: 0%] [G loss: 1.738402]
--------------- Epoch 7 ---------------
7 [D loss: 16.000975, acc: 0%] [G loss: 1.659525]
--------------- Epoch 8 ---------------
8 [D loss: 15.822689, acc: 0%] [G loss: 1.688146]
--------------- Epoch 9 ---------------
9 [D loss: 15.992999, acc: 0%] [G loss: 1.382316]
--------------- Epoch 10 ---------------
10 [D loss: 16.414194, acc: 0%] [G loss: 1.730018]
--------------- Epoch 11 ---------------
11 [D loss: 16.422037, acc: 0%] [G loss: 1.625704]
--------------- Epoch 12 ---------------
12 [D loss: 15.927192, acc: 0%] [G loss: 1.571837]
--------------- Epoch 13 ---------------
13 [D loss: 15.931881, acc: 0%] [G loss: 1.561636]
--------------- Epoch 14 ---------------
14 [D loss: 15.824091, acc: 0%] [G loss: 1.857262]
--------------- Epoch 15 ---------------
15 [D loss: 15.874186, acc: 0%] [G loss: 1.738947]
--------------- Epoch 16 ---------------
16 [D loss: 15.849995, acc: 0%] [G loss: 1.564434]
--------------- Epoch 17 ---------------
17 [D loss: 16.218544, acc: 0%] [G loss: 1.495176]
--------------- Epoch 18 ---------------
18 [D loss: 15.910048, acc: 0%] [G loss: 1.435223]
--------------- Epoch 19 ---------------
19 [D loss: 16.135427, acc: 0%] [G loss: 1.640984]
--------------- Epoch 20 ---------------
20 [D loss: 16.522854, acc: 0%] [G loss: 1.613840]
--------------- Epoch 21 ---------------
21 [D loss: 15.884249, acc: 0%] [G loss: 1.733757]
--------------- Epoch 22 ---------------
22 [D loss: 16.150993, acc: 0%] [G loss: 1.541417]
--------------- Epoch 23 ---------------
23 [D loss: 15.815909, acc: 0%] [G loss: 1.667652]
--------------- Epoch 24 ---------------
24 [D loss: 15.748118, acc: 0%] [G loss: 1.614371]
--------------- Epoch 25 ---------------
25 [D loss: 15.841260, acc: 0%] [G loss: 1.716248]
--------------- Epoch 26 ---------------
26 [D loss: 15.468090, acc: 0%] [G loss: 1.464901]
--------------- Epoch 27 ---------------
27 [D loss: 16.004213, acc: 0%] [G loss: 1.392183]
--------------- Epoch 28 ---------------
28 [D loss: 15.844113, acc: 0%] [G loss: 1.724826]
--------------- Epoch 29 ---------------
29 [D loss: 15.962990, acc: 0%] [G loss: 1.841937]
--------------- Epoch 30 ---------------
30 [D loss: 15.932665, acc: 0%] [G loss: 1.630792]
--------------- Epoch 31 ---------------
31 [D loss: 16.104147, acc: 0%] [G loss: 1.888526]
--------------- Epoch 32 ---------------
32 [D loss: 15.941484, acc: 0%] [G loss: 1.470259]
--------------- Epoch 33 ---------------
33 [D loss: 15.753471, acc: 0%] [G loss: 1.445539]
--------------- Epoch 34 ---------------
34 [D loss: 15.957688, acc: 0%] [G loss: 1.641849]
--------------- Epoch 35 ---------------
35 [D loss: 16.124573, acc: 0%] [G loss: 2.020876]
--------------- Epoch 36 ---------------
36 [D loss: 15.900434, acc: 0%] [G loss: 1.311012]
--------------- Epoch 37 ---------------
37 [D loss: 15.975209, acc: 0%] [G loss: 1.447426]
--------------- Epoch 38 ---------------
38 [D loss: 15.479131, acc: 0%] [G loss: 1.542337]
--------------- Epoch 39 ---------------
39 [D loss: 16.035606, acc: 0%] [G loss: 1.541560]
--------------- Epoch 40 ---------------
40 [D loss: 16.117737, acc: 0%] [G loss: 1.835258]
--------------- Epoch 41 ---------------
41 [D loss: 16.060230, acc: 0%] [G loss: 1.687586]
--------------- Epoch 42 ---------------
42 [D loss: 15.918585, acc: 0%] [G loss: 1.658261]
--------------- Epoch 43 ---------------
43 [D loss: 15.868798, acc: 0%] [G loss: 1.678880]
--------------- Epoch 44 ---------------
44 [D loss: 15.973824, acc: 0%] [G loss: 1.536244]
--------------- Epoch 45 ---------------
45 [D loss: 16.068510, acc: 0%] [G loss: 1.306865]
--------------- Epoch 46 ---------------
46 [D loss: 15.818397, acc: 0%] [G loss: 1.539119]
--------------- Epoch 47 ---------------
47 [D loss: 15.683941, acc: 0%] [G loss: 1.426421]
--------------- Epoch 48 ---------------
48 [D loss: 16.171970, acc: 0%] [G loss: 1.579335]
--------------- Epoch 49 ---------------
49 [D loss: 15.844611, acc: 0%] [G loss: 1.635070]
--------------- Epoch 50 ---------------
50 [D loss: 15.745028, acc: 0%] [G loss: 1.875839]
--------------- Epoch 51 ---------------
51 [D loss: 16.233210, acc: 0%] [G loss: 1.502618]
--------------- Epoch 52 ---------------
52 [D loss: 15.947420, acc: 0%] [G loss: 1.654209]
--------------- Epoch 53 ---------------
53 [D loss: 15.876113, acc: 0%] [G loss: 1.564896]
--------------- Epoch 54 ---------------
54 [D loss: 15.616358, acc: 0%] [G loss: 1.738046]
--------------- Epoch 55 ---------------
55 [D loss: 15.892595, acc: 0%] [G loss: 1.593264]
--------------- Epoch 56 ---------------
56 [D loss: 16.131721, acc: 0%] [G loss: 1.802465]
--------------- Epoch 57 ---------------
57 [D loss: 15.354412, acc: 0%] [G loss: 1.258142]
--------------- Epoch 58 ---------------
58 [D loss: 15.435907, acc: 0%] [G loss: 1.403479]
--------------- Epoch 59 ---------------
59 [D loss: 16.131264, acc: 0%] [G loss: 1.468593]
--------------- Epoch 60 ---------------
60 [D loss: 15.860603, acc: 0%] [G loss: 1.563378]
--------------- Epoch 61 ---------------
61 [D loss: 15.627783, acc: 0%] [G loss: 1.435684]
--------------- Epoch 62 ---------------
62 [D loss: 16.528872, acc: 0%] [G loss: 1.419364]
--------------- Epoch 63 ---------------
63 [D loss: 16.103197, acc: 0%] [G loss: 1.781819]
--------------- Epoch 64 ---------------
64 [D loss: 16.082451, acc: 0%] [G loss: 1.597065]
--------------- Epoch 65 ---------------
65 [D loss: 15.670720, acc: 0%] [G loss: 1.325220]
--------------- Epoch 66 ---------------
66 [D loss: 16.052933, acc: 0%] [G loss: 1.805313]
--------------- Epoch 67 ---------------
67 [D loss: 15.849991, acc: 0%] [G loss: 1.597997]
--------------- Epoch 68 ---------------
68 [D loss: 15.838813, acc: 0%] [G loss: 1.533128]
--------------- Epoch 69 ---------------
69 [D loss: 15.740126, acc: 0%] [G loss: 1.742261]
--------------- Epoch 70 ---------------
70 [D loss: 15.605111, acc: 0%] [G loss: 1.579644]
--------------- Epoch 71 ---------------
71 [D loss: 15.479339, acc: 0%] [G loss: 1.571836]
--------------- Epoch 72 ---------------
72 [D loss: 16.133656, acc: 0%] [G loss: 1.559739]
--------------- Epoch 73 ---------------
73 [D loss: 15.738238, acc: 0%] [G loss: 1.419723]
--------------- Epoch 74 ---------------
74 [D loss: 15.789889, acc: 0%] [G loss: 1.432984]
--------------- Epoch 75 ---------------
75 [D loss: 15.822619, acc: 0%] [G loss: 1.674646]
--------------- Epoch 76 ---------------
76 [D loss: 16.132158, acc: 0%] [G loss: 1.822799]
--------------- Epoch 77 ---------------
77 [D loss: 15.568275, acc: 0%] [G loss: 1.560884]
--------------- Epoch 78 ---------------
78 [D loss: 15.985308, acc: 0%] [G loss: 1.597505]
--------------- Epoch 79 ---------------
79 [D loss: 15.918224, acc: 0%] [G loss: 1.556579]
--------------- Epoch 80 ---------------
80 [D loss: 16.017408, acc: 0%] [G loss: 1.650240]
--------------- Epoch 81 ---------------
81 [D loss: 15.462349, acc: 0%] [G loss: 1.720449]
--------------- Epoch 82 ---------------
82 [D loss: 15.891733, acc: 0%] [G loss: 1.649269]
--------------- Epoch 83 ---------------
83 [D loss: 15.527041, acc: 0%] [G loss: 1.582831]
--------------- Epoch 84 ---------------
84 [D loss: 16.028708, acc: 0%] [G loss: 1.694007]
--------------- Epoch 85 ---------------
85 [D loss: 16.071205, acc: 0%] [G loss: 1.736534]
--------------- Epoch 86 ---------------
86 [D loss: 16.163845, acc: 0%] [G loss: 1.802009]
--------------- Epoch 87 ---------------
87 [D loss: 15.948998, acc: 0%] [G loss: 1.842725]
--------------- Epoch 88 ---------------
88 [D loss: 15.835043, acc: 0%] [G loss: 1.795006]
--------------- Epoch 89 ---------------
89 [D loss: 16.039095, acc: 0%] [G loss: 1.481435]
--------------- Epoch 90 ---------------
90 [D loss: 15.828753, acc: 0%] [G loss: 1.711043]
--------------- Epoch 91 ---------------
91 [D loss: 16.386923, acc: 0%] [G loss: 1.304413]
--------------- Epoch 92 ---------------
92 [D loss: 16.269794, acc: 0%] [G loss: 1.384383]
--------------- Epoch 93 ---------------
93 [D loss: 16.454449, acc: 0%] [G loss: 1.633529]
--------------- Epoch 94 ---------------
94 [D loss: 15.825148, acc: 0%] [G loss: 1.628955]
--------------- Epoch 95 ---------------
95 [D loss: 15.658757, acc: 0%] [G loss: 1.910015]
--------------- Epoch 96 ---------------
96 [D loss: 16.292141, acc: 0%] [G loss: 1.883774]
--------------- Epoch 97 ---------------
97 [D loss: 15.640224, acc: 0%] [G loss: 1.764425]
--------------- Epoch 98 ---------------
98 [D loss: 16.032282, acc: 0%] [G loss: 1.545317]
--------------- Epoch 99 ---------------
99 [D loss: 15.701758, acc: 0%] [G loss: 1.374132]
--------------- Epoch 100 ---------------
100 [D loss: 16.072603, acc: 0%] [G loss: 1.750533]
--------------- Epoch 101 ---------------
101 [D loss: 15.699347, acc: 0%] [G loss: 1.414578]
--------------- Epoch 102 ---------------
102 [D loss: 16.188700, acc: 0%] [G loss: 1.512618]
--------------- Epoch 103 ---------------
103 [D loss: 15.243372, acc: 0%] [G loss: 1.690634]
--------------- Epoch 104 ---------------
104 [D loss: 16.156603, acc: 0%] [G loss: 1.600227]
--------------- Epoch 105 ---------------
105 [D loss: 16.222734, acc: 0%] [G loss: 1.809938]
--------------- Epoch 106 ---------------
106 [D loss: 15.797258, acc: 0%] [G loss: 1.552202]
--------------- Epoch 107 ---------------
107 [D loss: 16.182501, acc: 0%] [G loss: 1.280400]
--------------- Epoch 108 ---------------
108 [D loss: 16.158503, acc: 0%] [G loss: 1.444895]
--------------- Epoch 109 ---------------
109 [D loss: 16.115856, acc: 0%] [G loss: 1.442582]
--------------- Epoch 110 ---------------
110 [D loss: 15.759265, acc: 0%] [G loss: 1.656623]
--------------- Epoch 111 ---------------
111 [D loss: 16.577076, acc: 0%] [G loss: 1.468884]
--------------- Epoch 112 ---------------
112 [D loss: 16.313873, acc: 0%] [G loss: 1.407014]
--------------- Epoch 113 ---------------
113 [D loss: 15.933405, acc: 0%] [G loss: 1.703715]
--------------- Epoch 114 ---------------
114 [D loss: 16.146631, acc: 0%] [G loss: 1.784615]
--------------- Epoch 115 ---------------
115 [D loss: 16.116781, acc: 0%] [G loss: 1.691926]
--------------- Epoch 116 ---------------
116 [D loss: 15.572573, acc: 0%] [G loss: 1.495829]
--------------- Epoch 117 ---------------
117 [D loss: 15.941835, acc: 0%] [G loss: 1.602516]
--------------- Epoch 118 ---------------
118 [D loss: 16.204018, acc: 0%] [G loss: 1.666633]
--------------- Epoch 119 ---------------
119 [D loss: 16.254486, acc: 0%] [G loss: 1.497062]
--------------- Epoch 120 ---------------
120 [D loss: 16.047779, acc: 0%] [G loss: 1.874111]
--------------- Epoch 121 ---------------
121 [D loss: 15.355593, acc: 0%] [G loss: 1.832829]
--------------- Epoch 122 ---------------
122 [D loss: 15.938095, acc: 0%] [G loss: 1.868617]
--------------- Epoch 123 ---------------
123 [D loss: 15.966650, acc: 0%] [G loss: 1.609529]
--------------- Epoch 124 ---------------
124 [D loss: 15.761163, acc: 0%] [G loss: 1.775553]
--------------- Epoch 125 ---------------
125 [D loss: 16.130604, acc: 0%] [G loss: 1.881685]
--------------- Epoch 126 ---------------
126 [D loss: 15.707820, acc: 0%] [G loss: 1.598988]
--------------- Epoch 127 ---------------
127 [D loss: 15.423105, acc: 0%] [G loss: 1.655819]
--------------- Epoch 128 ---------------
128 [D loss: 16.372639, acc: 0%] [G loss: 1.452529]
--------------- Epoch 129 ---------------
129 [D loss: 15.927251, acc: 0%] [G loss: 1.790789]
--------------- Epoch 130 ---------------
130 [D loss: 16.025644, acc: 0%] [G loss: 1.484674]
--------------- Epoch 131 ---------------
131 [D loss: 15.753783, acc: 0%] [G loss: 1.960452]
--------------- Epoch 132 ---------------
132 [D loss: 16.446951, acc: 0%] [G loss: 1.673705]
--------------- Epoch 133 ---------------
133 [D loss: 15.781274, acc: 0%] [G loss: 1.422341]
--------------- Epoch 134 ---------------
134 [D loss: 16.243361, acc: 0%] [G loss: 1.473229]
--------------- Epoch 135 ---------------
135 [D loss: 15.756761, acc: 0%] [G loss: 1.375454]
--------------- Epoch 136 ---------------
136 [D loss: 15.913621, acc: 0%] [G loss: 1.660900]
--------------- Epoch 137 ---------------
137 [D loss: 16.298512, acc: 0%] [G loss: 1.540828]
--------------- Epoch 138 ---------------
138 [D loss: 16.421669, acc: 0%] [G loss: 1.544639]
--------------- Epoch 139 ---------------
139 [D loss: 15.581784, acc: 0%] [G loss: 1.616474]
--------------- Epoch 140 ---------------
140 [D loss: 16.143709, acc: 0%] [G loss: 1.598524]
--------------- Epoch 141 ---------------
141 [D loss: 15.326074, acc: 0%] [G loss: 1.490039]
--------------- Epoch 142 ---------------
142 [D loss: 16.125032, acc: 0%] [G loss: 1.396585]
--------------- Epoch 143 ---------------
143 [D loss: 16.028164, acc: 0%] [G loss: 2.006952]
--------------- Epoch 144 ---------------
144 [D loss: 16.128471, acc: 0%] [G loss: 1.723709]
--------------- Epoch 145 ---------------
145 [D loss: 15.581550, acc: 0%] [G loss: 1.105382]
--------------- Epoch 146 ---------------
146 [D loss: 15.404273, acc: 0%] [G loss: 1.649745]
--------------- Epoch 147 ---------------
147 [D loss: 16.127052, acc: 0%] [G loss: 1.626780]
--------------- Epoch 148 ---------------
148 [D loss: 15.614319, acc: 0%] [G loss: 1.603069]
--------------- Epoch 149 ---------------
149 [D loss: 16.061348, acc: 0%] [G loss: 1.111699]
--------------- Epoch 150 ---------------
150 [D loss: 15.865358, acc: 0%] [G loss: 1.971372]
--------------- Epoch 151 ---------------
151 [D loss: 15.964150, acc: 0%] [G loss: 1.661410]
--------------- Epoch 152 ---------------
152 [D loss: 16.193913, acc: 0%] [G loss: 1.596449]
--------------- Epoch 153 ---------------
153 [D loss: 15.787200, acc: 0%] [G loss: 1.360591]
--------------- Epoch 154 ---------------
154 [D loss: 15.622075, acc: 0%] [G loss: 1.646922]
--------------- Epoch 155 ---------------
155 [D loss: 16.132669, acc: 0%] [G loss: 1.752008]
--------------- Epoch 156 ---------------
156 [D loss: 16.116842, acc: 0%] [G loss: 1.438424]
--------------- Epoch 157 ---------------
157 [D loss: 16.157698, acc: 0%] [G loss: 1.831171]
--------------- Epoch 158 ---------------
158 [D loss: 15.907785, acc: 0%] [G loss: 1.423223]
--------------- Epoch 159 ---------------
159 [D loss: 15.486089, acc: 0%] [G loss: 1.456410]
--------------- Epoch 160 ---------------
160 [D loss: 16.054947, acc: 0%] [G loss: 1.967921]
--------------- Epoch 161 ---------------
161 [D loss: 15.473951, acc: 0%] [G loss: 1.632475]
--------------- Epoch 162 ---------------
162 [D loss: 15.566884, acc: 0%] [G loss: 1.700008]
--------------- Epoch 163 ---------------
163 [D loss: 15.937019, acc: 0%] [G loss: 1.620687]
--------------- Epoch 164 ---------------
164 [D loss: 16.359596, acc: 0%] [G loss: 1.669458]
--------------- Epoch 165 ---------------
165 [D loss: 15.818108, acc: 0%] [G loss: 1.536847]
--------------- Epoch 166 ---------------
166 [D loss: 16.095661, acc: 0%] [G loss: 1.620180]
--------------- Epoch 167 ---------------
167 [D loss: 16.453651, acc: 0%] [G loss: 1.544162]
--------------- Epoch 168 ---------------
168 [D loss: 16.106148, acc: 0%] [G loss: 1.705709]
--------------- Epoch 169 ---------------
169 [D loss: 16.023212, acc: 0%] [G loss: 1.701271]
--------------- Epoch 170 ---------------
170 [D loss: 15.829756, acc: 0%] [G loss: 1.905116]
--------------- Epoch 171 ---------------
171 [D loss: 15.987038, acc: 0%] [G loss: 1.497768]
--------------- Epoch 172 ---------------
172 [D loss: 15.746178, acc: 0%] [G loss: 1.546332]
--------------- Epoch 173 ---------------
173 [D loss: 15.595694, acc: 0%] [G loss: 1.760994]
--------------- Epoch 174 ---------------
174 [D loss: 16.114672, acc: 0%] [G loss: 1.575463]
--------------- Epoch 175 ---------------
175 [D loss: 16.053982, acc: 0%] [G loss: 1.475420]
--------------- Epoch 176 ---------------
176 [D loss: 15.508592, acc: 0%] [G loss: 1.643948]
--------------- Epoch 177 ---------------
177 [D loss: 15.726924, acc: 0%] [G loss: 1.222505]
--------------- Epoch 178 ---------------
178 [D loss: 16.553244, acc: 0%] [G loss: 1.623582]
--------------- Epoch 179 ---------------
179 [D loss: 15.898852, acc: 0%] [G loss: 1.434817]
--------------- Epoch 180 ---------------
180 [D loss: 15.259079, acc: 0%] [G loss: 1.393316]
--------------- Epoch 181 ---------------
181 [D loss: 16.032766, acc: 0%] [G loss: 1.457490]
--------------- Epoch 182 ---------------
182 [D loss: 16.215372, acc: 0%] [G loss: 1.564255]
--------------- Epoch 183 ---------------
183 [D loss: 15.844995, acc: 0%] [G loss: 1.613432]
--------------- Epoch 184 ---------------
184 [D loss: 15.943069, acc: 0%] [G loss: 1.468913]
--------------- Epoch 185 ---------------
185 [D loss: 15.543568, acc: 0%] [G loss: 1.745055]
--------------- Epoch 186 ---------------
186 [D loss: 16.128973, acc: 0%] [G loss: 1.457973]
--------------- Epoch 187 ---------------
187 [D loss: 15.897048, acc: 0%] [G loss: 1.575821]
--------------- Epoch 188 ---------------
188 [D loss: 16.001322, acc: 0%] [G loss: 1.747514]
--------------- Epoch 189 ---------------
189 [D loss: 15.838546, acc: 0%] [G loss: 1.452338]
--------------- Epoch 190 ---------------
190 [D loss: 16.163689, acc: 0%] [G loss: 1.730777]
--------------- Epoch 191 ---------------
191 [D loss: 15.697495, acc: 0%] [G loss: 1.435136]
--------------- Epoch 192 ---------------
192 [D loss: 15.828603, acc: 0%] [G loss: 1.511776]
--------------- Epoch 193 ---------------
193 [D loss: 15.867246, acc: 0%] [G loss: 1.712705]
--------------- Epoch 194 ---------------
194 [D loss: 15.469098, acc: 0%] [G loss: 1.720956]
--------------- Epoch 195 ---------------
195 [D loss: 15.831402, acc: 0%] [G loss: 1.719032]
--------------- Epoch 196 ---------------
196 [D loss: 15.634424, acc: 0%] [G loss: 1.554823]
--------------- Epoch 197 ---------------
197 [D loss: 16.112116, acc: 0%] [G loss: 1.793299]
--------------- Epoch 198 ---------------
198 [D loss: 15.745666, acc: 0%] [G loss: 1.717282]
--------------- Epoch 199 ---------------
199 [D loss: 16.198694, acc: 0%] [G loss: 1.463763]
--------------- Epoch 200 ---------------
200 [D loss: 15.772562, acc: 0%] [G loss: 1.335691]
########## N units 32 ##########
generator
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_64 (LSTM)               (None, 50, 32)            4864      
_________________________________________________________________
dropout_61 (Dropout)         (None, 50, 32)            0         
_________________________________________________________________
time_distributed_38 (TimeDis (None, 50, 2)             66        
=================================================================
Total params: 4,930
Trainable params: 4,930
Non-trainable params: 0
_________________________________________________________________
None
discriminator
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_65 (LSTM)               (None, 50, 32)            4480      
_________________________________________________________________
dropout_62 (Dropout)         (None, 50, 32)            0         
_________________________________________________________________
time_distributed_39 (TimeDis (None, 50, 1)             33        
_________________________________________________________________
average_pooling1d_21 (Averag (None, 1, 1)              0         
_________________________________________________________________
flatten_21 (Flatten)         (None, 1)                 0         
=================================================================
Total params: 4,513
Trainable params: 4,513
Non-trainable params: 0
_________________________________________________________________
None
GAN
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_21 (InputLayer)        (None, 50, 5)             0         
_________________________________________________________________
sequential_41 (Sequential)   (None, 50, 2)             4930      
_________________________________________________________________
sequential_42 (Sequential)   (None, 1)                 4513      
=================================================================
Total params: 9,443
Trainable params: 4,930
Non-trainable params: 4,513
_________________________________________________________________
None
--------------- Epoch 1 ---------------
1 [D loss: 15.593517, acc: 0%] [G loss: 1.526654]
--------------- Epoch 2 ---------------
2 [D loss: 16.096497, acc: 0%] [G loss: 1.876264]
--------------- Epoch 3 ---------------
3 [D loss: 15.605979, acc: 0%] [G loss: 1.687673]
--------------- Epoch 4 ---------------
4 [D loss: 16.014881, acc: 0%] [G loss: 1.541707]
--------------- Epoch 5 ---------------
5 [D loss: 16.065197, acc: 0%] [G loss: 1.786612]
--------------- Epoch 6 ---------------
6 [D loss: 15.949964, acc: 0%] [G loss: 1.580287]
--------------- Epoch 7 ---------------
7 [D loss: 16.017786, acc: 0%] [G loss: 1.296432]
--------------- Epoch 8 ---------------
8 [D loss: 15.751572, acc: 0%] [G loss: 1.570674]
--------------- Epoch 9 ---------------
9 [D loss: 15.589145, acc: 0%] [G loss: 1.948883]
--------------- Epoch 10 ---------------
10 [D loss: 16.017305, acc: 0%] [G loss: 1.538626]
--------------- Epoch 11 ---------------
11 [D loss: 15.602583, acc: 0%] [G loss: 1.514958]
--------------- Epoch 12 ---------------
12 [D loss: 15.632116, acc: 0%] [G loss: 1.809081]
--------------- Epoch 13 ---------------
13 [D loss: 16.163862, acc: 0%] [G loss: 1.722496]
--------------- Epoch 14 ---------------
14 [D loss: 16.001236, acc: 0%] [G loss: 1.608462]
--------------- Epoch 15 ---------------
15 [D loss: 15.891337, acc: 0%] [G loss: 1.728987]
--------------- Epoch 16 ---------------
16 [D loss: 16.035801, acc: 0%] [G loss: 1.665848]
--------------- Epoch 17 ---------------
17 [D loss: 16.027008, acc: 0%] [G loss: 1.457250]
--------------- Epoch 18 ---------------
18 [D loss: 15.835633, acc: 0%] [G loss: 1.516970]
--------------- Epoch 19 ---------------
19 [D loss: 16.409822, acc: 0%] [G loss: 1.657275]
--------------- Epoch 20 ---------------
20 [D loss: 16.029375, acc: 0%] [G loss: 1.375527]
--------------- Epoch 21 ---------------
21 [D loss: 16.339220, acc: 0%] [G loss: 1.481162]
--------------- Epoch 22 ---------------
22 [D loss: 16.377769, acc: 0%] [G loss: 1.839374]
--------------- Epoch 23 ---------------
23 [D loss: 15.702494, acc: 0%] [G loss: 1.561293]
--------------- Epoch 24 ---------------
24 [D loss: 15.835626, acc: 0%] [G loss: 1.433126]
--------------- Epoch 25 ---------------
25 [D loss: 15.968641, acc: 0%] [G loss: 1.900828]
--------------- Epoch 26 ---------------
26 [D loss: 16.000811, acc: 0%] [G loss: 1.433431]
--------------- Epoch 27 ---------------
27 [D loss: 15.760777, acc: 0%] [G loss: 1.220659]
--------------- Epoch 28 ---------------
28 [D loss: 16.009895, acc: 0%] [G loss: 1.457658]
--------------- Epoch 29 ---------------
29 [D loss: 15.121134, acc: 0%] [G loss: 1.275207]
--------------- Epoch 30 ---------------
30 [D loss: 15.728733, acc: 0%] [G loss: 1.218367]
--------------- Epoch 31 ---------------
31 [D loss: 15.941820, acc: 0%] [G loss: 1.688352]
--------------- Epoch 32 ---------------
32 [D loss: 16.187447, acc: 0%] [G loss: 1.569467]
--------------- Epoch 33 ---------------
33 [D loss: 15.791378, acc: 0%] [G loss: 1.634581]
--------------- Epoch 34 ---------------
34 [D loss: 15.676977, acc: 0%] [G loss: 1.475374]
--------------- Epoch 35 ---------------
35 [D loss: 16.412676, acc: 0%] [G loss: 1.528075]
--------------- Epoch 36 ---------------
36 [D loss: 16.010355, acc: 0%] [G loss: 1.742446]
--------------- Epoch 37 ---------------
37 [D loss: 16.242792, acc: 0%] [G loss: 1.456485]
--------------- Epoch 38 ---------------
38 [D loss: 16.165876, acc: 0%] [G loss: 1.616068]
--------------- Epoch 39 ---------------
39 [D loss: 15.975800, acc: 0%] [G loss: 1.589519]
--------------- Epoch 40 ---------------
40 [D loss: 15.620242, acc: 0%] [G loss: 1.473394]
--------------- Epoch 41 ---------------
41 [D loss: 15.692784, acc: 0%] [G loss: 1.428399]
--------------- Epoch 42 ---------------
42 [D loss: 15.405291, acc: 0%] [G loss: 1.327662]
--------------- Epoch 43 ---------------
43 [D loss: 16.013445, acc: 0%] [G loss: 1.523982]
--------------- Epoch 44 ---------------
44 [D loss: 15.989591, acc: 0%] [G loss: 1.525766]
--------------- Epoch 45 ---------------
45 [D loss: 16.170994, acc: 0%] [G loss: 1.555665]
--------------- Epoch 46 ---------------
46 [D loss: 16.082003, acc: 0%] [G loss: 1.100746]
--------------- Epoch 47 ---------------
47 [D loss: 16.304482, acc: 0%] [G loss: 1.600717]
--------------- Epoch 48 ---------------
48 [D loss: 15.652458, acc: 0%] [G loss: 1.713997]
--------------- Epoch 49 ---------------
49 [D loss: 16.007582, acc: 0%] [G loss: 1.508082]
--------------- Epoch 50 ---------------
50 [D loss: 15.699226, acc: 0%] [G loss: 1.533642]
--------------- Epoch 51 ---------------
51 [D loss: 15.891225, acc: 0%] [G loss: 1.906308]
--------------- Epoch 52 ---------------
52 [D loss: 15.880632, acc: 0%] [G loss: 1.698630]
--------------- Epoch 53 ---------------
53 [D loss: 16.101948, acc: 0%] [G loss: 1.610256]
--------------- Epoch 54 ---------------
54 [D loss: 15.410490, acc: 0%] [G loss: 1.599067]
--------------- Epoch 55 ---------------
55 [D loss: 16.311209, acc: 0%] [G loss: 1.601876]
--------------- Epoch 56 ---------------
56 [D loss: 15.917598, acc: 0%] [G loss: 1.673215]
--------------- Epoch 57 ---------------
57 [D loss: 15.738845, acc: 0%] [G loss: 1.316845]
--------------- Epoch 58 ---------------
58 [D loss: 15.857004, acc: 0%] [G loss: 1.755125]
--------------- Epoch 59 ---------------
59 [D loss: 15.946133, acc: 0%] [G loss: 1.582383]
--------------- Epoch 60 ---------------
60 [D loss: 15.721088, acc: 0%] [G loss: 1.613655]
--------------- Epoch 61 ---------------
61 [D loss: 15.941074, acc: 0%] [G loss: 1.352039]
--------------- Epoch 62 ---------------
62 [D loss: 15.904973, acc: 0%] [G loss: 1.529946]
--------------- Epoch 63 ---------------
63 [D loss: 15.906208, acc: 0%] [G loss: 1.379418]
--------------- Epoch 64 ---------------
64 [D loss: 15.658485, acc: 0%] [G loss: 1.767989]
--------------- Epoch 65 ---------------
65 [D loss: 15.871434, acc: 0%] [G loss: 1.778733]
--------------- Epoch 66 ---------------
66 [D loss: 16.055790, acc: 0%] [G loss: 1.571452]
--------------- Epoch 67 ---------------
67 [D loss: 16.053242, acc: 0%] [G loss: 1.624800]
--------------- Epoch 68 ---------------
68 [D loss: 15.696156, acc: 0%] [G loss: 1.752360]
--------------- Epoch 69 ---------------
69 [D loss: 15.859665, acc: 0%] [G loss: 1.642939]
--------------- Epoch 70 ---------------
70 [D loss: 15.998986, acc: 0%] [G loss: 1.526549]
--------------- Epoch 71 ---------------
71 [D loss: 15.863653, acc: 0%] [G loss: 1.433268]
--------------- Epoch 72 ---------------
72 [D loss: 16.373768, acc: 0%] [G loss: 1.510736]
--------------- Epoch 73 ---------------
73 [D loss: 16.065121, acc: 0%] [G loss: 1.762412]
--------------- Epoch 74 ---------------
74 [D loss: 16.024538, acc: 0%] [G loss: 1.539191]
--------------- Epoch 75 ---------------
75 [D loss: 16.248272, acc: 0%] [G loss: 1.544494]
--------------- Epoch 76 ---------------
76 [D loss: 16.076317, acc: 0%] [G loss: 1.692682]
--------------- Epoch 77 ---------------
77 [D loss: 15.786634, acc: 0%] [G loss: 1.787209]
--------------- Epoch 78 ---------------
78 [D loss: 15.749854, acc: 0%] [G loss: 1.647909]
--------------- Epoch 79 ---------------
79 [D loss: 16.354509, acc: 0%] [G loss: 1.683064]
--------------- Epoch 80 ---------------
80 [D loss: 16.029913, acc: 0%] [G loss: 1.761456]
--------------- Epoch 81 ---------------
81 [D loss: 15.500829, acc: 0%] [G loss: 1.453768]
--------------- Epoch 82 ---------------
82 [D loss: 15.925123, acc: 0%] [G loss: 1.598755]
--------------- Epoch 83 ---------------
83 [D loss: 15.825201, acc: 0%] [G loss: 1.686084]
--------------- Epoch 84 ---------------
84 [D loss: 15.466615, acc: 0%] [G loss: 1.341886]
--------------- Epoch 85 ---------------
85 [D loss: 16.208792, acc: 0%] [G loss: 1.595200]
--------------- Epoch 86 ---------------
86 [D loss: 15.939857, acc: 0%] [G loss: 1.557899]
--------------- Epoch 87 ---------------
87 [D loss: 15.892132, acc: 0%] [G loss: 1.707782]
--------------- Epoch 88 ---------------
88 [D loss: 15.903750, acc: 0%] [G loss: 1.722343]
--------------- Epoch 89 ---------------
89 [D loss: 15.632763, acc: 0%] [G loss: 1.537262]
--------------- Epoch 90 ---------------
90 [D loss: 16.169655, acc: 0%] [G loss: 1.827452]
--------------- Epoch 91 ---------------
91 [D loss: 15.800654, acc: 0%] [G loss: 1.561767]
--------------- Epoch 92 ---------------
92 [D loss: 16.041355, acc: 0%] [G loss: 1.259755]
--------------- Epoch 93 ---------------
93 [D loss: 16.278471, acc: 0%] [G loss: 1.706467]
--------------- Epoch 94 ---------------
94 [D loss: 15.610088, acc: 0%] [G loss: 1.820223]
--------------- Epoch 95 ---------------
95 [D loss: 15.855645, acc: 0%] [G loss: 1.462147]
--------------- Epoch 96 ---------------
96 [D loss: 16.366308, acc: 0%] [G loss: 1.772851]
--------------- Epoch 97 ---------------
97 [D loss: 15.862999, acc: 0%] [G loss: 1.664302]
--------------- Epoch 98 ---------------
98 [D loss: 15.910274, acc: 0%] [G loss: 1.511374]
--------------- Epoch 99 ---------------
99 [D loss: 16.404058, acc: 0%] [G loss: 1.483157]
--------------- Epoch 100 ---------------
100 [D loss: 15.733566, acc: 0%] [G loss: 1.379910]
--------------- Epoch 101 ---------------
101 [D loss: 15.736397, acc: 0%] [G loss: 1.423396]
--------------- Epoch 102 ---------------
102 [D loss: 15.857459, acc: 0%] [G loss: 1.709476]
--------------- Epoch 103 ---------------
103 [D loss: 16.012468, acc: 0%] [G loss: 1.969780]
--------------- Epoch 104 ---------------
104 [D loss: 15.737567, acc: 0%] [G loss: 1.550623]
--------------- Epoch 105 ---------------
105 [D loss: 15.794437, acc: 0%] [G loss: 1.790568]
--------------- Epoch 106 ---------------
106 [D loss: 15.924973, acc: 0%] [G loss: 1.690751]
--------------- Epoch 107 ---------------
107 [D loss: 15.719218, acc: 0%] [G loss: 1.427481]
--------------- Epoch 108 ---------------
108 [D loss: 16.258343, acc: 0%] [G loss: 1.587215]
--------------- Epoch 109 ---------------
109 [D loss: 16.244408, acc: 0%] [G loss: 1.906476]
--------------- Epoch 110 ---------------
110 [D loss: 15.903549, acc: 0%] [G loss: 1.647676]
--------------- Epoch 111 ---------------
111 [D loss: 16.016352, acc: 0%] [G loss: 1.801856]
--------------- Epoch 112 ---------------
112 [D loss: 15.718854, acc: 0%] [G loss: 1.520201]
--------------- Epoch 113 ---------------
113 [D loss: 15.952146, acc: 0%] [G loss: 1.539811]
--------------- Epoch 114 ---------------
114 [D loss: 16.225552, acc: 0%] [G loss: 1.471911]
--------------- Epoch 115 ---------------
115 [D loss: 15.733477, acc: 0%] [G loss: 1.945024]
--------------- Epoch 116 ---------------
116 [D loss: 16.136080, acc: 0%] [G loss: 1.754574]
--------------- Epoch 117 ---------------
117 [D loss: 15.977145, acc: 0%] [G loss: 1.668159]
--------------- Epoch 118 ---------------
118 [D loss: 16.049406, acc: 0%] [G loss: 1.583157]
--------------- Epoch 119 ---------------
119 [D loss: 15.330503, acc: 0%] [G loss: 1.583531]
--------------- Epoch 120 ---------------
120 [D loss: 15.871789, acc: 0%] [G loss: 1.215462]
--------------- Epoch 121 ---------------
121 [D loss: 15.912038, acc: 0%] [G loss: 1.408618]
--------------- Epoch 122 ---------------
122 [D loss: 15.560574, acc: 0%] [G loss: 1.459442]
--------------- Epoch 123 ---------------
123 [D loss: 16.069984, acc: 0%] [G loss: 1.860148]
--------------- Epoch 124 ---------------
124 [D loss: 16.248764, acc: 0%] [G loss: 1.268758]
--------------- Epoch 125 ---------------
125 [D loss: 16.229454, acc: 0%] [G loss: 1.634041]
--------------- Epoch 126 ---------------
126 [D loss: 16.413284, acc: 0%] [G loss: 1.420234]
--------------- Epoch 127 ---------------
127 [D loss: 15.673954, acc: 0%] [G loss: 1.605676]
--------------- Epoch 128 ---------------
128 [D loss: 15.823676, acc: 0%] [G loss: 1.889498]
--------------- Epoch 129 ---------------
129 [D loss: 15.816111, acc: 0%] [G loss: 1.378890]
--------------- Epoch 130 ---------------
130 [D loss: 15.880013, acc: 0%] [G loss: 1.542909]
--------------- Epoch 131 ---------------
131 [D loss: 16.269768, acc: 0%] [G loss: 1.329509]
--------------- Epoch 132 ---------------
132 [D loss: 16.089855, acc: 0%] [G loss: 1.760567]
--------------- Epoch 133 ---------------
133 [D loss: 15.559754, acc: 0%] [G loss: 1.844723]
--------------- Epoch 134 ---------------
134 [D loss: 15.896524, acc: 0%] [G loss: 1.924232]
--------------- Epoch 135 ---------------
135 [D loss: 16.731573, acc: 0%] [G loss: 1.697971]
--------------- Epoch 136 ---------------
136 [D loss: 16.697296, acc: 0%] [G loss: 1.576626]
--------------- Epoch 137 ---------------
137 [D loss: 15.894840, acc: 0%] [G loss: 1.777692]
--------------- Epoch 138 ---------------
138 [D loss: 15.776573, acc: 0%] [G loss: 1.906960]
--------------- Epoch 139 ---------------
139 [D loss: 15.930618, acc: 0%] [G loss: 1.452888]
--------------- Epoch 140 ---------------
140 [D loss: 16.121452, acc: 0%] [G loss: 1.443377]
--------------- Epoch 141 ---------------
141 [D loss: 16.348385, acc: 0%] [G loss: 1.408008]
--------------- Epoch 142 ---------------
142 [D loss: 15.964083, acc: 0%] [G loss: 1.646885]
--------------- Epoch 143 ---------------
143 [D loss: 15.885070, acc: 0%] [G loss: 1.626783]
--------------- Epoch 144 ---------------
144 [D loss: 15.872102, acc: 0%] [G loss: 1.470548]
--------------- Epoch 145 ---------------
145 [D loss: 16.652235, acc: 0%] [G loss: 1.712988]
--------------- Epoch 146 ---------------
146 [D loss: 16.353491, acc: 0%] [G loss: 1.710233]
--------------- Epoch 147 ---------------
147 [D loss: 15.861475, acc: 0%] [G loss: 1.444224]
--------------- Epoch 148 ---------------
148 [D loss: 16.106487, acc: 0%] [G loss: 1.598367]
--------------- Epoch 149 ---------------
149 [D loss: 15.868346, acc: 0%] [G loss: 1.388390]
--------------- Epoch 150 ---------------
150 [D loss: 15.604406, acc: 0%] [G loss: 1.398530]
--------------- Epoch 151 ---------------
151 [D loss: 15.753942, acc: 0%] [G loss: 1.700093]
--------------- Epoch 152 ---------------
152 [D loss: 15.855124, acc: 0%] [G loss: 1.543508]
--------------- Epoch 153 ---------------
153 [D loss: 15.814215, acc: 0%] [G loss: 1.436910]
--------------- Epoch 154 ---------------
154 [D loss: 15.957285, acc: 0%] [G loss: 1.640898]
--------------- Epoch 155 ---------------
155 [D loss: 16.211514, acc: 0%] [G loss: 1.489065]
--------------- Epoch 156 ---------------
156 [D loss: 15.824558, acc: 0%] [G loss: 2.070922]
--------------- Epoch 157 ---------------
157 [D loss: 16.068790, acc: 0%] [G loss: 1.855699]
--------------- Epoch 158 ---------------
158 [D loss: 15.849420, acc: 0%] [G loss: 1.468984]
--------------- Epoch 159 ---------------
159 [D loss: 16.255329, acc: 0%] [G loss: 1.532225]
--------------- Epoch 160 ---------------
160 [D loss: 16.548918, acc: 0%] [G loss: 1.590760]
--------------- Epoch 161 ---------------
161 [D loss: 15.697768, acc: 0%] [G loss: 1.797382]
--------------- Epoch 162 ---------------
162 [D loss: 15.509221, acc: 0%] [G loss: 1.298065]
--------------- Epoch 163 ---------------
163 [D loss: 15.612037, acc: 0%] [G loss: 1.706306]
--------------- Epoch 164 ---------------
164 [D loss: 16.214521, acc: 0%] [G loss: 1.559510]
--------------- Epoch 165 ---------------
165 [D loss: 15.843839, acc: 0%] [G loss: 1.611494]
--------------- Epoch 166 ---------------
166 [D loss: 15.916102, acc: 0%] [G loss: 1.297909]
--------------- Epoch 167 ---------------
167 [D loss: 16.225847, acc: 0%] [G loss: 1.695442]
--------------- Epoch 168 ---------------
168 [D loss: 15.929594, acc: 0%] [G loss: 1.852150]
--------------- Epoch 169 ---------------
169 [D loss: 15.626062, acc: 0%] [G loss: 1.394384]
--------------- Epoch 170 ---------------
170 [D loss: 16.102661, acc: 0%] [G loss: 1.819010]
--------------- Epoch 171 ---------------
171 [D loss: 16.155357, acc: 0%] [G loss: 1.619435]
--------------- Epoch 172 ---------------
172 [D loss: 15.638395, acc: 0%] [G loss: 1.472422]
--------------- Epoch 173 ---------------
173 [D loss: 15.697719, acc: 0%] [G loss: 1.745772]
--------------- Epoch 174 ---------------
174 [D loss: 15.643806, acc: 0%] [G loss: 1.703757]
--------------- Epoch 175 ---------------
175 [D loss: 15.875886, acc: 0%] [G loss: 1.742798]
--------------- Epoch 176 ---------------
176 [D loss: 15.927452, acc: 0%] [G loss: 2.043914]
--------------- Epoch 177 ---------------
177 [D loss: 15.690166, acc: 0%] [G loss: 1.793196]
--------------- Epoch 178 ---------------
178 [D loss: 15.960705, acc: 0%] [G loss: 1.726211]
--------------- Epoch 179 ---------------
179 [D loss: 15.693514, acc: 0%] [G loss: 1.657142]
--------------- Epoch 180 ---------------
180 [D loss: 15.856592, acc: 0%] [G loss: 1.640172]
--------------- Epoch 181 ---------------
181 [D loss: 15.848130, acc: 0%] [G loss: 1.614639]
--------------- Epoch 182 ---------------
182 [D loss: 16.186926, acc: 0%] [G loss: 1.917035]
--------------- Epoch 183 ---------------
183 [D loss: 15.760093, acc: 0%] [G loss: 1.577630]
--------------- Epoch 184 ---------------
184 [D loss: 15.934226, acc: 0%] [G loss: 1.502025]
--------------- Epoch 185 ---------------
185 [D loss: 15.471413, acc: 0%] [G loss: 1.432598]
--------------- Epoch 186 ---------------
186 [D loss: 15.810109, acc: 0%] [G loss: 1.699244]
--------------- Epoch 187 ---------------
187 [D loss: 16.013363, acc: 0%] [G loss: 1.822517]
--------------- Epoch 188 ---------------
188 [D loss: 16.401882, acc: 0%] [G loss: 1.742430]
--------------- Epoch 189 ---------------
189 [D loss: 16.275211, acc: 0%] [G loss: 1.401868]
--------------- Epoch 190 ---------------
190 [D loss: 15.532837, acc: 0%] [G loss: 1.949976]
--------------- Epoch 191 ---------------
191 [D loss: 16.155693, acc: 0%] [G loss: 1.587784]
--------------- Epoch 192 ---------------
192 [D loss: 16.157253, acc: 0%] [G loss: 2.195089]
--------------- Epoch 193 ---------------
193 [D loss: 15.983495, acc: 0%] [G loss: 1.839056]
--------------- Epoch 194 ---------------
194 [D loss: 16.163221, acc: 0%] [G loss: 1.465165]
--------------- Epoch 195 ---------------
195 [D loss: 15.922903, acc: 0%] [G loss: 1.406927]
--------------- Epoch 196 ---------------
196 [D loss: 15.976225, acc: 0%] [G loss: 1.323443]
--------------- Epoch 197 ---------------
197 [D loss: 15.971405, acc: 0%] [G loss: 1.647563]
--------------- Epoch 198 ---------------
198 [D loss: 15.229906, acc: 0%] [G loss: 1.691268]
--------------- Epoch 199 ---------------
199 [D loss: 15.833257, acc: 0%] [G loss: 1.398200]
--------------- Epoch 200 ---------------
200 [D loss: 15.445384, acc: 0%] [G loss: 1.465361]
########## N units 64 ##########
generator
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_66 (LSTM)               (None, 50, 64)            17920     
_________________________________________________________________
dropout_63 (Dropout)         (None, 50, 64)            0         
_________________________________________________________________
time_distributed_40 (TimeDis (None, 50, 2)             130       
=================================================================
Total params: 18,050
Trainable params: 18,050
Non-trainable params: 0
_________________________________________________________________
None
discriminator
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_67 (LSTM)               (None, 50, 64)            17152     
_________________________________________________________________
dropout_64 (Dropout)         (None, 50, 64)            0         
_________________________________________________________________
time_distributed_41 (TimeDis (None, 50, 1)             65        
_________________________________________________________________
average_pooling1d_22 (Averag (None, 1, 1)              0         
_________________________________________________________________
flatten_22 (Flatten)         (None, 1)                 0         
=================================================================
Total params: 17,217
Trainable params: 17,217
Non-trainable params: 0
_________________________________________________________________
None
GAN
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_22 (InputLayer)        (None, 50, 5)             0         
_________________________________________________________________
sequential_43 (Sequential)   (None, 50, 2)             18050     
_________________________________________________________________
sequential_44 (Sequential)   (None, 1)                 17217     
=================================================================
Total params: 35,267
Trainable params: 18,050
Non-trainable params: 17,217
_________________________________________________________________
None
--------------- Epoch 1 ---------------
1 [D loss: 15.919559, acc: 0%] [G loss: 1.408713]
--------------- Epoch 2 ---------------
2 [D loss: 15.662755, acc: 0%] [G loss: 1.560920]
--------------- Epoch 3 ---------------
3 [D loss: 15.431039, acc: 0%] [G loss: 1.588288]
--------------- Epoch 4 ---------------
4 [D loss: 15.650792, acc: 0%] [G loss: 1.511834]
--------------- Epoch 5 ---------------
5 [D loss: 16.036526, acc: 0%] [G loss: 1.614751]
--------------- Epoch 6 ---------------
6 [D loss: 16.429308, acc: 0%] [G loss: 1.623236]
--------------- Epoch 7 ---------------
7 [D loss: 16.224594, acc: 0%] [G loss: 1.451097]
--------------- Epoch 8 ---------------
8 [D loss: 15.922357, acc: 0%] [G loss: 1.614773]
--------------- Epoch 9 ---------------
9 [D loss: 15.848824, acc: 0%] [G loss: 1.516115]
--------------- Epoch 10 ---------------
10 [D loss: 15.360256, acc: 0%] [G loss: 1.541890]
--------------- Epoch 11 ---------------
11 [D loss: 15.901499, acc: 0%] [G loss: 1.499716]
--------------- Epoch 12 ---------------
12 [D loss: 15.737226, acc: 0%] [G loss: 1.661892]
--------------- Epoch 13 ---------------
13 [D loss: 15.601904, acc: 0%] [G loss: 1.500616]
--------------- Epoch 14 ---------------
14 [D loss: 15.710213, acc: 0%] [G loss: 1.637842]
--------------- Epoch 15 ---------------
15 [D loss: 16.046261, acc: 0%] [G loss: 1.617872]
--------------- Epoch 16 ---------------
16 [D loss: 16.112753, acc: 0%] [G loss: 1.914837]
--------------- Epoch 17 ---------------
17 [D loss: 16.076302, acc: 0%] [G loss: 1.583585]
--------------- Epoch 18 ---------------
18 [D loss: 15.821032, acc: 0%] [G loss: 1.833083]
--------------- Epoch 19 ---------------
19 [D loss: 15.995583, acc: 0%] [G loss: 1.509647]
--------------- Epoch 20 ---------------
20 [D loss: 15.991502, acc: 0%] [G loss: 1.975930]
--------------- Epoch 21 ---------------
21 [D loss: 15.865708, acc: 0%] [G loss: 1.573542]
--------------- Epoch 22 ---------------
22 [D loss: 15.632617, acc: 0%] [G loss: 1.653433]
--------------- Epoch 23 ---------------
23 [D loss: 15.443535, acc: 0%] [G loss: 1.452587]
--------------- Epoch 24 ---------------
24 [D loss: 15.702106, acc: 0%] [G loss: 1.789833]
--------------- Epoch 25 ---------------
25 [D loss: 15.975422, acc: 0%] [G loss: 2.002794]
--------------- Epoch 26 ---------------
26 [D loss: 15.974027, acc: 0%] [G loss: 1.859814]
--------------- Epoch 27 ---------------
27 [D loss: 16.192223, acc: 0%] [G loss: 1.525907]
--------------- Epoch 28 ---------------
28 [D loss: 16.139206, acc: 0%] [G loss: 1.738470]
--------------- Epoch 29 ---------------
29 [D loss: 16.077127, acc: 0%] [G loss: 1.470239]
--------------- Epoch 30 ---------------
30 [D loss: 16.097239, acc: 0%] [G loss: 1.462672]
--------------- Epoch 31 ---------------
31 [D loss: 15.978188, acc: 0%] [G loss: 1.493746]
--------------- Epoch 32 ---------------
32 [D loss: 15.813176, acc: 0%] [G loss: 1.477322]
--------------- Epoch 33 ---------------
33 [D loss: 16.082872, acc: 0%] [G loss: 1.793746]
--------------- Epoch 34 ---------------
34 [D loss: 16.215590, acc: 0%] [G loss: 1.688084]
--------------- Epoch 35 ---------------
35 [D loss: 16.030882, acc: 0%] [G loss: 1.949533]
--------------- Epoch 36 ---------------
36 [D loss: 15.719958, acc: 0%] [G loss: 1.333581]
--------------- Epoch 37 ---------------
37 [D loss: 15.886467, acc: 0%] [G loss: 1.372330]
--------------- Epoch 38 ---------------
38 [D loss: 15.657475, acc: 0%] [G loss: 1.609871]
--------------- Epoch 39 ---------------
39 [D loss: 15.903374, acc: 0%] [G loss: 1.645880]
--------------- Epoch 40 ---------------
40 [D loss: 16.195477, acc: 0%] [G loss: 1.755054]
--------------- Epoch 41 ---------------
41 [D loss: 16.177351, acc: 0%] [G loss: 1.830838]
--------------- Epoch 42 ---------------
42 [D loss: 15.609878, acc: 0%] [G loss: 1.832396]
--------------- Epoch 43 ---------------
43 [D loss: 15.872885, acc: 0%] [G loss: 1.624275]
--------------- Epoch 44 ---------------
44 [D loss: 16.496944, acc: 0%] [G loss: 1.418241]
--------------- Epoch 45 ---------------
45 [D loss: 16.056620, acc: 0%] [G loss: 1.342654]
--------------- Epoch 46 ---------------
46 [D loss: 15.571651, acc: 0%] [G loss: 1.667595]
--------------- Epoch 47 ---------------
47 [D loss: 15.274197, acc: 0%] [G loss: 1.435638]
--------------- Epoch 48 ---------------
48 [D loss: 15.998304, acc: 0%] [G loss: 1.367238]
--------------- Epoch 49 ---------------
49 [D loss: 15.954995, acc: 0%] [G loss: 1.612247]
--------------- Epoch 50 ---------------
50 [D loss: 15.886927, acc: 0%] [G loss: 1.589144]
--------------- Epoch 51 ---------------
51 [D loss: 16.070580, acc: 0%] [G loss: 1.345038]
--------------- Epoch 52 ---------------
52 [D loss: 16.437822, acc: 0%] [G loss: 1.680984]
--------------- Epoch 53 ---------------
53 [D loss: 16.119823, acc: 0%] [G loss: 1.750123]
--------------- Epoch 54 ---------------
54 [D loss: 15.953939, acc: 0%] [G loss: 1.176481]
--------------- Epoch 55 ---------------
55 [D loss: 16.192568, acc: 0%] [G loss: 1.624898]
--------------- Epoch 56 ---------------
56 [D loss: 15.947477, acc: 0%] [G loss: 1.796298]
--------------- Epoch 57 ---------------
57 [D loss: 15.668966, acc: 0%] [G loss: 1.590094]
--------------- Epoch 58 ---------------
58 [D loss: 16.531397, acc: 0%] [G loss: 1.290874]
--------------- Epoch 59 ---------------
59 [D loss: 15.541404, acc: 0%] [G loss: 1.531675]
--------------- Epoch 60 ---------------
60 [D loss: 16.459610, acc: 0%] [G loss: 1.234609]
--------------- Epoch 61 ---------------
61 [D loss: 15.842582, acc: 0%] [G loss: 1.759958]
--------------- Epoch 62 ---------------
62 [D loss: 15.963490, acc: 0%] [G loss: 1.646479]
--------------- Epoch 63 ---------------
63 [D loss: 16.212824, acc: 0%] [G loss: 1.288865]
--------------- Epoch 64 ---------------
64 [D loss: 15.997873, acc: 0%] [G loss: 1.673416]
--------------- Epoch 65 ---------------
65 [D loss: 15.987232, acc: 0%] [G loss: 1.498348]
--------------- Epoch 66 ---------------
66 [D loss: 15.819345, acc: 0%] [G loss: 1.726533]
--------------- Epoch 67 ---------------
67 [D loss: 15.762268, acc: 0%] [G loss: 1.701906]
--------------- Epoch 68 ---------------
68 [D loss: 16.070845, acc: 0%] [G loss: 1.300885]
--------------- Epoch 69 ---------------
69 [D loss: 15.955468, acc: 0%] [G loss: 1.336236]
--------------- Epoch 70 ---------------
70 [D loss: 16.229549, acc: 0%] [G loss: 1.540080]
--------------- Epoch 71 ---------------
71 [D loss: 15.918986, acc: 0%] [G loss: 1.812367]
--------------- Epoch 72 ---------------
72 [D loss: 15.756455, acc: 0%] [G loss: 1.570190]
--------------- Epoch 73 ---------------
73 [D loss: 16.181068, acc: 0%] [G loss: 1.799763]
--------------- Epoch 74 ---------------
74 [D loss: 16.035162, acc: 0%] [G loss: 1.468481]
--------------- Epoch 75 ---------------
75 [D loss: 16.155268, acc: 0%] [G loss: 1.657441]
--------------- Epoch 76 ---------------
76 [D loss: 15.991613, acc: 0%] [G loss: 1.622384]
--------------- Epoch 77 ---------------
77 [D loss: 15.633555, acc: 0%] [G loss: 1.511959]
--------------- Epoch 78 ---------------
78 [D loss: 15.998829, acc: 0%] [G loss: 1.589947]
--------------- Epoch 79 ---------------
79 [D loss: 15.907543, acc: 0%] [G loss: 1.405650]
--------------- Epoch 80 ---------------
80 [D loss: 15.900762, acc: 0%] [G loss: 1.445697]
--------------- Epoch 81 ---------------
81 [D loss: 16.084301, acc: 0%] [G loss: 1.465347]
--------------- Epoch 82 ---------------
82 [D loss: 15.903255, acc: 0%] [G loss: 1.498177]
--------------- Epoch 83 ---------------
83 [D loss: 16.452591, acc: 0%] [G loss: 0.819480]
--------------- Epoch 84 ---------------
84 [D loss: 15.814860, acc: 0%] [G loss: 1.639249]
--------------- Epoch 85 ---------------
85 [D loss: 16.092628, acc: 0%] [G loss: 1.300482]
--------------- Epoch 86 ---------------
86 [D loss: 15.731277, acc: 0%] [G loss: 1.378483]
--------------- Epoch 87 ---------------
87 [D loss: 15.833070, acc: 0%] [G loss: 1.355763]
--------------- Epoch 88 ---------------
88 [D loss: 15.448348, acc: 0%] [G loss: 1.428129]
--------------- Epoch 89 ---------------
89 [D loss: 15.981375, acc: 0%] [G loss: 1.760863]
--------------- Epoch 90 ---------------
90 [D loss: 16.203699, acc: 0%] [G loss: 1.602599]
--------------- Epoch 91 ---------------
91 [D loss: 16.193975, acc: 0%] [G loss: 1.233378]
--------------- Epoch 92 ---------------
92 [D loss: 15.885591, acc: 0%] [G loss: 1.898912]
--------------- Epoch 93 ---------------
93 [D loss: 15.869398, acc: 0%] [G loss: 1.700226]
--------------- Epoch 94 ---------------
94 [D loss: 16.181063, acc: 0%] [G loss: 1.022392]
--------------- Epoch 95 ---------------
95 [D loss: 16.074770, acc: 0%] [G loss: 1.560534]
--------------- Epoch 96 ---------------
96 [D loss: 15.658441, acc: 0%] [G loss: 1.342826]
--------------- Epoch 97 ---------------
97 [D loss: 15.712294, acc: 0%] [G loss: 1.579782]
--------------- Epoch 98 ---------------
98 [D loss: 15.899879, acc: 0%] [G loss: 1.941209]
--------------- Epoch 99 ---------------
99 [D loss: 15.742855, acc: 0%] [G loss: 1.518406]
--------------- Epoch 100 ---------------
100 [D loss: 15.465937, acc: 0%] [G loss: 1.666050]
--------------- Epoch 101 ---------------
101 [D loss: 15.935230, acc: 0%] [G loss: 1.690354]
--------------- Epoch 102 ---------------
102 [D loss: 16.093264, acc: 0%] [G loss: 1.679998]
--------------- Epoch 103 ---------------
103 [D loss: 15.868736, acc: 0%] [G loss: 2.038069]
--------------- Epoch 104 ---------------
104 [D loss: 15.761224, acc: 0%] [G loss: 1.562603]
--------------- Epoch 105 ---------------
105 [D loss: 15.971940, acc: 0%] [G loss: 1.785896]
--------------- Epoch 106 ---------------
106 [D loss: 15.790472, acc: 0%] [G loss: 1.437379]
--------------- Epoch 107 ---------------
107 [D loss: 16.004271, acc: 0%] [G loss: 1.447667]
--------------- Epoch 108 ---------------
108 [D loss: 16.068192, acc: 0%] [G loss: 1.519467]
--------------- Epoch 109 ---------------
109 [D loss: 16.100119, acc: 0%] [G loss: 1.574075]
--------------- Epoch 110 ---------------
110 [D loss: 15.807134, acc: 0%] [G loss: 1.527737]
--------------- Epoch 111 ---------------
111 [D loss: 15.700272, acc: 0%] [G loss: 1.750741]
--------------- Epoch 112 ---------------
112 [D loss: 15.879895, acc: 0%] [G loss: 1.372429]
--------------- Epoch 113 ---------------
113 [D loss: 15.881604, acc: 0%] [G loss: 1.218755]
--------------- Epoch 114 ---------------
114 [D loss: 16.046307, acc: 0%] [G loss: 1.534242]
--------------- Epoch 115 ---------------
115 [D loss: 15.595766, acc: 0%] [G loss: 1.596093]
--------------- Epoch 116 ---------------
116 [D loss: 16.051966, acc: 0%] [G loss: 1.531015]
--------------- Epoch 117 ---------------
117 [D loss: 16.218559, acc: 0%] [G loss: 1.477148]
--------------- Epoch 118 ---------------
118 [D loss: 16.026295, acc: 0%] [G loss: 1.501458]
--------------- Epoch 119 ---------------
119 [D loss: 16.059650, acc: 0%] [G loss: 1.341848]
--------------- Epoch 120 ---------------
120 [D loss: 16.135519, acc: 0%] [G loss: 1.336418]
--------------- Epoch 121 ---------------
121 [D loss: 15.838229, acc: 0%] [G loss: 1.206351]
--------------- Epoch 122 ---------------
122 [D loss: 15.719488, acc: 0%] [G loss: 1.515192]
--------------- Epoch 123 ---------------
123 [D loss: 16.174820, acc: 0%] [G loss: 1.793731]
--------------- Epoch 124 ---------------
124 [D loss: 15.903509, acc: 0%] [G loss: 1.861339]
--------------- Epoch 125 ---------------
125 [D loss: 16.044739, acc: 0%] [G loss: 1.546402]
--------------- Epoch 126 ---------------
126 [D loss: 16.516518, acc: 0%] [G loss: 1.650944]
--------------- Epoch 127 ---------------
127 [D loss: 15.959034, acc: 0%] [G loss: 1.363625]
--------------- Epoch 128 ---------------
128 [D loss: 15.794064, acc: 0%] [G loss: 1.900916]
--------------- Epoch 129 ---------------
129 [D loss: 15.534899, acc: 0%] [G loss: 1.601925]
--------------- Epoch 130 ---------------
130 [D loss: 16.212870, acc: 0%] [G loss: 1.520173]
--------------- Epoch 131 ---------------
131 [D loss: 16.174942, acc: 0%] [G loss: 1.625221]
--------------- Epoch 132 ---------------
132 [D loss: 15.530769, acc: 0%] [G loss: 1.470120]
--------------- Epoch 133 ---------------
133 [D loss: 15.818734, acc: 0%] [G loss: 1.574998]
--------------- Epoch 134 ---------------
134 [D loss: 15.849533, acc: 0%] [G loss: 1.874682]
--------------- Epoch 135 ---------------
135 [D loss: 15.960468, acc: 0%] [G loss: 1.175879]
--------------- Epoch 136 ---------------
136 [D loss: 15.754844, acc: 0%] [G loss: 1.642629]
--------------- Epoch 137 ---------------
137 [D loss: 16.200893, acc: 0%] [G loss: 1.693633]
--------------- Epoch 138 ---------------
138 [D loss: 16.238588, acc: 0%] [G loss: 1.656381]
--------------- Epoch 139 ---------------
139 [D loss: 16.136482, acc: 0%] [G loss: 1.739968]
--------------- Epoch 140 ---------------
140 [D loss: 15.800255, acc: 0%] [G loss: 1.721807]
--------------- Epoch 141 ---------------
141 [D loss: 16.200911, acc: 0%] [G loss: 1.711083]
--------------- Epoch 142 ---------------
142 [D loss: 16.102509, acc: 0%] [G loss: 1.579713]
--------------- Epoch 143 ---------------
143 [D loss: 15.474085, acc: 0%] [G loss: 1.650602]
--------------- Epoch 144 ---------------
144 [D loss: 15.259395, acc: 0%] [G loss: 1.849665]
--------------- Epoch 145 ---------------
145 [D loss: 16.267006, acc: 0%] [G loss: 1.647975]
--------------- Epoch 146 ---------------
146 [D loss: 15.809683, acc: 0%] [G loss: 1.593054]
--------------- Epoch 147 ---------------
147 [D loss: 15.714068, acc: 0%] [G loss: 1.319760]
--------------- Epoch 148 ---------------
148 [D loss: 15.913931, acc: 0%] [G loss: 1.492082]
--------------- Epoch 149 ---------------
149 [D loss: 16.102200, acc: 0%] [G loss: 1.805519]
--------------- Epoch 150 ---------------
150 [D loss: 15.832059, acc: 0%] [G loss: 1.743266]
--------------- Epoch 151 ---------------
151 [D loss: 16.200043, acc: 0%] [G loss: 1.433404]
--------------- Epoch 152 ---------------
152 [D loss: 15.706372, acc: 0%] [G loss: 1.300685]
--------------- Epoch 153 ---------------
153 [D loss: 15.704783, acc: 0%] [G loss: 1.457997]
--------------- Epoch 154 ---------------
154 [D loss: 15.379781, acc: 0%] [G loss: 1.534380]
--------------- Epoch 155 ---------------
155 [D loss: 16.095020, acc: 0%] [G loss: 1.811668]
--------------- Epoch 156 ---------------
156 [D loss: 15.672408, acc: 0%] [G loss: 1.761770]
--------------- Epoch 157 ---------------
157 [D loss: 16.242956, acc: 0%] [G loss: 1.570962]
--------------- Epoch 158 ---------------
158 [D loss: 15.767841, acc: 0%] [G loss: 1.511964]
--------------- Epoch 159 ---------------
159 [D loss: 15.929812, acc: 0%] [G loss: 1.399764]
--------------- Epoch 160 ---------------
160 [D loss: 15.741736, acc: 0%] [G loss: 1.549856]
--------------- Epoch 161 ---------------
161 [D loss: 16.283358, acc: 0%] [G loss: 1.447834]
--------------- Epoch 162 ---------------
162 [D loss: 16.089096, acc: 0%] [G loss: 1.377613]
--------------- Epoch 163 ---------------
163 [D loss: 15.515229, acc: 0%] [G loss: 1.642902]
--------------- Epoch 164 ---------------
164 [D loss: 16.041775, acc: 0%] [G loss: 1.564546]
--------------- Epoch 165 ---------------
165 [D loss: 16.070126, acc: 0%] [G loss: 1.683293]
--------------- Epoch 166 ---------------
166 [D loss: 16.182945, acc: 0%] [G loss: 1.402201]
--------------- Epoch 167 ---------------
167 [D loss: 15.576828, acc: 0%] [G loss: 1.783360]
--------------- Epoch 168 ---------------
168 [D loss: 15.834699, acc: 0%] [G loss: 1.703666]
--------------- Epoch 169 ---------------
169 [D loss: 15.748823, acc: 0%] [G loss: 1.544891]
--------------- Epoch 170 ---------------
170 [D loss: 15.833146, acc: 0%] [G loss: 1.786496]
--------------- Epoch 171 ---------------
171 [D loss: 16.348080, acc: 0%] [G loss: 1.701656]
--------------- Epoch 172 ---------------
172 [D loss: 16.056671, acc: 0%] [G loss: 1.885243]
--------------- Epoch 173 ---------------
173 [D loss: 15.697167, acc: 0%] [G loss: 1.460023]
--------------- Epoch 174 ---------------
174 [D loss: 15.922980, acc: 0%] [G loss: 1.621078]
--------------- Epoch 175 ---------------
175 [D loss: 16.255064, acc: 0%] [G loss: 1.739458]
--------------- Epoch 176 ---------------
176 [D loss: 15.866642, acc: 0%] [G loss: 1.512241]
--------------- Epoch 177 ---------------
177 [D loss: 16.262564, acc: 0%] [G loss: 1.479320]
--------------- Epoch 178 ---------------
178 [D loss: 16.151318, acc: 0%] [G loss: 1.851243]
--------------- Epoch 179 ---------------
179 [D loss: 15.864118, acc: 0%] [G loss: 1.598876]
--------------- Epoch 180 ---------------
180 [D loss: 16.254538, acc: 0%] [G loss: 1.591578]
--------------- Epoch 181 ---------------
181 [D loss: 16.030603, acc: 0%] [G loss: 1.611927]
--------------- Epoch 182 ---------------
182 [D loss: 15.662592, acc: 0%] [G loss: 1.599955]
--------------- Epoch 183 ---------------
183 [D loss: 16.118004, acc: 0%] [G loss: 1.535650]
--------------- Epoch 184 ---------------
184 [D loss: 15.843777, acc: 0%] [G loss: 1.397636]
--------------- Epoch 185 ---------------
185 [D loss: 16.091202, acc: 0%] [G loss: 1.753410]
--------------- Epoch 186 ---------------
186 [D loss: 15.630193, acc: 0%] [G loss: 1.592940]
--------------- Epoch 187 ---------------
187 [D loss: 16.116283, acc: 0%] [G loss: 1.407438]
--------------- Epoch 188 ---------------
188 [D loss: 15.738949, acc: 0%] [G loss: 1.523242]
--------------- Epoch 189 ---------------
189 [D loss: 16.001057, acc: 0%] [G loss: 1.555120]
--------------- Epoch 190 ---------------
190 [D loss: 15.990858, acc: 0%] [G loss: 1.739020]
--------------- Epoch 191 ---------------
191 [D loss: 15.916818, acc: 0%] [G loss: 1.718347]
--------------- Epoch 192 ---------------
192 [D loss: 15.815706, acc: 0%] [G loss: 1.368264]
--------------- Epoch 193 ---------------
193 [D loss: 15.776003, acc: 0%] [G loss: 1.733399]
--------------- Epoch 194 ---------------
194 [D loss: 15.709784, acc: 0%] [G loss: 1.681009]
--------------- Epoch 195 ---------------
195 [D loss: 15.728071, acc: 0%] [G loss: 1.456653]
--------------- Epoch 196 ---------------
196 [D loss: 15.660630, acc: 0%] [G loss: 1.503509]
--------------- Epoch 197 ---------------
197 [D loss: 15.724030, acc: 0%] [G loss: 1.652249]
--------------- Epoch 198 ---------------
198 [D loss: 15.823837, acc: 0%] [G loss: 1.452961]
--------------- Epoch 199 ---------------
199 [D loss: 15.691634, acc: 0%] [G loss: 1.605959]
--------------- Epoch 200 ---------------
200 [D loss: 15.860080, acc: 0%] [G loss: 1.566014]